Monographie
Lambert, P., Couplet, E., Verleysen, M., & Lee, J. (2025). Neighbour Embeddings: Beyond Visualisation. The Eurographics Association. https://doi.org/10.2312/mlvis.20251156
Article de journal
Lee, J., Couplet, E., Lambert, P. H., Merveille, P., Journaux, L., Mulders, D., de Bodt, C., & Verleysen, M. (2025). Improving on early exaggeration in t-SNE: early hierarchization better preserves global structure. Neurocomputing, 1(1), 131882. https://doi.org/10.1016/j.neucom.2025.131882 (Original work published 2025)
Papier de conférence
Lee, J., Lambert, P., Couplet, E., Merveille, P., Journaux, L., Mulders, D., de Bodt, C., & Verleysen, M. (2025). Can MDS rival with t-SNE by using the symmetric Kullback-Leibler divergence across neighborhoods as a pseudo-distance? ESANN 2025 proceedings. Published. 33th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium. https://doi.org/10.14428/esann/2025.ES2025-174
Article de journal
Couplet, E., Lambert, P., Verleysen, M., Lee, J., & De Bodt, C. (2024). Investigating latent representations and generalization in deep neural networks for tabular data. Neurocomputing, 597C. https://doi.org/10.1016/j.neucom.2024.127967 (Original work published 2024)
Papier de conférence
Lee, J., Couplet, E., Lambert, P., Journaux, L., Mulders, D., de Bodt, C., & Verleysen, M. (2024). Forget early exaggeration in t-SNE: early hierarchization preserves global structure. ESANN 2024 proceedings. Published. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Papier de conférence
Couplet, E., Lambert, P., Verleysen, M., Lee, J., & De Bodt, C. (2023). On the number of latent representations in deep neural networks for tabular data. ESANN proceedings, 1(1), 1-6. https://doi.org/10.14428/esann/2023.ES2023-156 (Original work published 2023)
Germany Morrison, E. I., Teixeira, I., Danthine, V., Verleysen, M., Nonclercq, A., & El Tahry, R. (2023). Exploring graph-derived metrics from functional brain connectivity analyses from PDC and DTF connectomes as VNS outcome predictors. Proceedings of the ILAE 35th International Epilepsy Congress.
Couplet, E., Lambert, P., Verleysen, M., Mulders, D., Lee, J., & De Bodt, C. (2023). Natively Interpretable t-SNE. Proceedings of AIMLAI workshop, 1(1), 1-16. (Original work published 2023)
Article de journal
van den Elzen, S., Andrienko, G., Andrienko, N., Fisher, B. D., Martins, R. M., Peltonen, J., Telea, A. C., & Verleysen, M. (2023). The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications. IEEE Computer Graphics and Applications, 43(2), 78-88. (Original work published 2023)
Serna-Serna, W., De Bodt, C., Andres M. Alvarez-Meza, Lee, J., Verleysen, M., & Alvaro A. Orozco-Gutierrez. (2023). Semi-supervised t-SNE with multi-scale neighborhood preservation. Neurocomputing, 550(1), 126496. https://doi.org/10.1016/j.neucom.2023.126496 (Original work published 2023)
Germany Morrison, E. I., Teixeira, I., Danthine, V., Santalucia, R., Cakiroglu, I., Torres Sánchez, A., Verleysen, M., Delbeke, J., Nonclercq, A., & El Tahry, R. (2023). Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors. Journal of Neural Engineering, 20(4), 46039. https://doi.org/10.1088/1741-2552/acf1cd (Original work published 2023)
Article de journal
Lambert, P., De Bodt, C., Verleysen, M., Lee, J., & et al. (2022). SQuadMDS: a lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP. Neurocomputing, 503, 17-27. (Original work published 2022)
Papier de conférence
Lambert, P., De Bodt, C., Verleysen, M., & Lee, J. (2021). Stochastic quartet approach for fast multidimensional scaling. ESANN 2021 proceedings, 417-422.
Lambert, P., Lee, J., Verleysen, M., & De Bodt, C. (2021). Impact of data subsamplings in Fast Multi-Scale Neighbor Embedding. ESANN 2021 proceedings, 435-440.
Monographie
Verleysen, M. (2021). ESANN 2021, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2021: Proceedings.
Article de journal
Degeest, A., Frénay, B., & Verleysen, M. (2021). Reading grid for feature selection relevance criteria in regression. Pattern Recognition Letters, 148, 92-99. https://doi.org/10.1016/j.patrec.2021.04.031 (Original work published 2021)
Article de journal
De Bodt, C., Mulders, D., Verleysen, M., & Lee, J. (2020). Fast Multiscale Neighbor Embedding. I E E E Transactions on Neural Networks and Learning Systems, 33(4), 1546-1560. https://doi.org/10.1109/TNNLS.2020.3042807 (Original work published 2022)
Mulders, D., De Bodt, C., Lejeune, N., Courtin, A., Liberati, G., Verleysen, M., & Mouraux, A. (2020). Dynamics of the perception and EEG signals triggered by tonic warm and cool stimulation. PLoS One, 15(4), e0231698 [1-25]. https://doi.org/10.1371/journal.pone.0231698 (Original work published 2020)
Mulders, D., De Bodt, C., Bjelland, J., Pentland, A., Verleysen, M., & de Montjoye, Y.-A. (2020). Inference of node attributes from social network assortativity. Neural Computing and Applications, 32, 18023-18043. https://doi.org/10.1007/s00521-018-03967-z (Original work published 2020)
Coelho, F., Costa, M., Verleysen, M., & Braga, A. P. (2020). LASSO multi-objective learning algorithm for feature selection. Soft Computing, 24(4), 9. https://doi.org/10.1007/s00500-020-04734-w (Original work published 2020)
Papier de conférence
Valy, D., Verleysen, M., & Chhun, S. (2020). Data Augmentation and Text Recognition on Khmer Historical Manuscripts. 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), Dortmund (Germany).
Crecchi Francesco, De Bodt, C., Verleysen, M., Lee, J., & Bacciu Davide. (2020). Perplexity-free Parametric t-SNE. ESANN 2020 proceedings, p. 387-392.
Monographie
Verleysen, M. (2020). 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Papier de conférence
Degeest, A., Verleysen, M., & Frénay, B. (2019). Comparison Between Filter Criteria for Feature Selection in Regression. Lecture Notes in Computer Science. Published. ICANN 2019, Munich. (Original work published 2019)
Mulders, D., De Bodt, C., Lejeune, N., Lee, J., Mouraux, A., & Verleysen, M. (2019). Tensor factorization to extract patterns in multimodal EEG data. ESANN 2019 proceedings, 601-606.
de Smet, D., Francaux, M., Baijot, L., & Verleysen, M. (2019). MAP Best Performances Prediction for Endurance Runners. 2019 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019), Bruges (Belgium).
Valy, D., Verleysen, M., & Chhun, S. (2019). Text Recognition on Khmer Historical Documents using Glyph Class Map Generation with Encoder-Decoder Model. Proceedings of ICPRAM 2019, 8. (Original work published 2018)
Degeest, A., Verleysen, M., & Frénay, B. (2019). About Filter Criteria for Feature Selection in Regression. Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, 11507(48), 579-590. (Original work published 2019)
Monographie
Verleysen, M. (2019). 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Article de journal
De Bodt, C., Mulders, D., Verleysen, M., & Lee, J. (2019). Nonlinear Dimensionality Reduction with Missing Data using Parametric Multiple Imputations. I E E E Transactions on Neural Networks and Learning Systems, 30(4), 1166-1179. https://doi.org/10.1109/TNNLS.2018.2861891 (Original work published 2019)
Papier de conférence
Degeest, A., Verleysen, M., & Frénay, B. (2018). Smoothness Bias in Relevance Estimators for Feature Selection in Regression. IFIPAICT, 519, 285-294. https://doi.org/10.1007/978-3-319-92007-8_25 (Original work published 2018)
Mulders, D., De Bodt, C., Lejeune, N., Mouraux, A., & Verleysen, M. (2018). Linear Periodic Discriminant Analysis of Multidimensional Signals. In Cheng L., Leung A., Ozawa S. (ed.), Neural Information Processing (pp. 476-487). https://doi.org/10.1007/978-3-030-04224-0_41
Valy, D., Verleysen, M., & Burie, J.-C. (2018). Character and Text Recognition of Khmer Historical Palm Leaf Manuscripts. Proceedings of ICFHR 2018, 6. https://doi.org/10.1109/ICFHR-2018.2018.00012 (Original work published 2018)
Kesiman, M. W. A., Valy, D., Burie, J.-C., Paulus, E., Suryani, M., Hadi, S., Verleysen, M., Chhun, S., & Ogier, J.-M. (2018). ICFHR 2018 Competition On Document Image Analysis Tasks for Southeast Asian Palm Leaf Manuscripts. Proceedings of ICFHR 2018, 483-488. https://doi.org/10.1109/ICFHR-2018.2018.00090 (Original work published 2018)
De Bodt, C., Mulders, D., Verleysen, M., & Lee, J. (2018). Perplexity-free t-SNE and twice Student tt-SNE. ESANN 2018 proceedings, 123-128.
de Smet, D., Verleysen, M., Francaux, M., & Baijot, L. (2018). Long-Distance Running Routes’ Flat Equivalent Distances from Race Results and Elevation Profiles. Proceedings of the 6th International Congress on Sport Sciences Research and Technology Support. Published. 6th International Congress on Sport Sciences Research and Technology Support, Seville, Spain. https://doi.org/10.5220/0006937000560062
Article de journal
Kesiman, M. W. A., Valy, D., Burie, J.-C., Paulus, E., Suryani, M., Hadi, S., Verleysen, M., Chhun, S., & Ogier, J.-M. (2018). Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia. Journal of Imaging, 4(43), 27. https://doi.org/10.3390/jimaging4020043 (Original work published 2018)
Document de travail
Feraud, B., Munaut, C., Martin, M., Verleysen, M., & Govaerts, B. (2017). Combining strong sparsity and competitive predictive power with the L-sOPLS approach for biomarker discovery in metabolomics (ISBA Discussion Paper 2017/20).
Papier de conférence
Mulders, D., De Bodt, C., Bjelland, J., Pentland, A. S., Verleysen, M., & de Montjoye, Y.-A. (2017). Improving individual predictions using social networks assortativity. The Benelearn 2017 Proceedings, 134-136.
Valy, D., Verleysen, M., Chhun, S., & Burie, J.-C. (2017). A New Khmer Palm Leaf Manuscript Dataset for Document Analysis and Recognition - SleukRith Set. 4th International Workshop on Historical Document Imaging and Processing (HIP) at ICDAR2017, Kyoto (Japan).
Valy, D., Verleysen, M., & SOK, K. (2017). Line Segmentation for Grayscale Text Images of Khmer Palm Leaf Manuscripts. 7th International Conference on Image Processing Theory, Tools & Applications (IPTA), Montréal (Canada).
Mulders, D., De Bodt, C., Bjelland, J., Pentland, A. (., Verleysen, M., & de Montjoye, Y.-A. (2017). Improving Individual Predictions using Social Networks Assortativity. 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+), 127-134. https://doi.org/10.1109/WSOM.2017.8020023
de Smet, D., Verleysen, M., & Francaux, M. (2017). Running Race Times Prediction and Runner Performances Comparison Using a Matrix Factorization Approach. Proceedings of the 5th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS. Published. 5th International Congress on Sport Sciences Research and Technology Support, Funchal, Madeira, Portugal. https://doi.org/10.5220/0006499300960101
Article de journal
Alvarez-Meza, A. M., Lee, J., Verleysen, M., & Castellanos-Dominguez, G. (2017). Kernel-based dimensionality reduction using Renyi’s α-entropy measures of similarity. Neurocomputing, 222, 36-46. https://doi.org/10.1016/j.neucom.2016.10.004 (Original work published 2017)
Coelho, F., Castro, C., Braga, A. P., & Verleysen, M. (2017). Semi-supervised relevance index for feature selection. Neural Computing and Applications, 31, 989-997. https://doi.org/10.1007/s00521-017-3062-0 (Original work published 2017)
El Mahrsi, M. K., Come, E., Oukhellou, L., & Verleysen, M. (2017). Clustering smart card data for urban mobility analysis. IEEE Transactions on Intelligent Transportation Systems, 18(3), 712-728. https://doi.org/10.1109/tits.2016.2600515 (Original work published 2017)
Feraud, B., Rousseau, R., Tullio, P. d., Verleysen, M., & Govaerts, B. (2017). Independent Component Analysis and Statistical Modelling for the Identification of Metabolomics Biomarkers in 1H-NMR Spectroscopy. Journal of Biometrics & Biostatistics, 8(4 (2017)). https://doi.org/10.4172/2155-6180.1000367 (Original work published 2017)
Feraud, B., Munaut, C., Martin, M., Verleysen, M., & Govaerts, B. (2017). Combining strong sparsity and competitive predictive power with the L-sOPLS approach for biomarker discovery in metabolomics. Metabolomics, 13(130), 15. https://doi.org/10.1007/s11306-017-1275-y (Original work published 2017)
Papier de conférence
Valy, D., Verleysen, M., & Sok, K. (2016). Line Segmentation Approach for Ancient Palm Leaf Manuscripts using Competitive Learning Algorithm. 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen (China).
Garcia Vega, S., Castellanos-Dominguez, G., Verleysen, M., & Lee, J. (2016). Multi-step-ahead forecasting using kernel adaptive filtering. 2016 International Joint Conference on Neural Networks (IJCNN), 2132-2139. https://doi.org/10.1109/IJCNN.2016.7727463 (Original work published 2016)
de Smet, D., Francaux, M., Hendrickx, J., & Verleysen, M. (2016). Heart rate modelling as a potential physical fitness assessment for runners and cyclists. ECML-PKDD, Riva Del Garda, Italy.
Papier de conférence
Chuor, P., Verleysen, M., & Valy, D. (2015). Khmer Optical Character Recognition Using Zernike Moment. 2015 Khmer Natural Language Processing annual conference (KNLP 2015), Phnom Penh (Cambodia).
Degeest, A., Verleysen, M., & Frénay, B. (2015). Feature Ranking in Changing Environments where New Features are Introduced. Proceedings of IJCNN 2015, 1-8. https://doi.org/10.1109/IJCNN.2015.7280533
Billiet, L., Hunyadi, B., Matic, V., Van Huffel, S., & Verleysen, M. (2015). Single trial classification in Mobile BCI - A multiway Kernel approach. Proceedings of BIOSIGNALS 2015, 5-11. https://doi.org/10.5220/0005163000050011
Peluffo Ordoñez, D. H., Lee, J., Verleysen, M., Rodriguez, J. L., & Castellanos-Dominguez, G. (2015). Unsupervised relevance analysis for feature extraction and selection. A distance-based approach for feature relevance. 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014), Angers (France).
Peluffo-Ordonez, D. H., Alvarado-Perez, J. C., Lee, J., & Verleysen, M. (2015). Geometrical homotopy for data visualization. Proceedings of ESANN 2015, 525-530.
Chapitre de livre
Peluffo Ordoñez, D. H., Lee, J., Verleysen, M., & Alvarado-Pérez, J. C. (2015). Geometrical homotopy for data visualization. In ESANN 2015 - 23rd Eur. Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. p. 525-530). D-side.
Article de journal
Lee, J., Peluffo-Ordóñez, D. H., & Verleysen, M. (2015). Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure. Neurocomputing, 169, 246-261. https://doi.org/10.1016/j.neucom.2014.12.095 (Original work published 2015)
Frénay, B., & Verleysen, M. (2015). Classification in the Presence of Label Noise: a Survey. I E E E Transactions on Neural Networks and Learning Systems, 25(5), 845-869. https://doi.org/10.1109/TNNLS.2013.2292894 (Original work published 2015)
Frénay, B., & Verleysen, M. (2015). Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers. I E E E Transactions on Cybernetics, 99, 13. https://doi.org/10.1109/TCYB.2015.2504404 (Original work published 2015)
Feraud, B., Govaerts, B., Verleysen, M., & de Tullio, P. (2015). Statistical treatment of 2D NMR COSY spectra in metabolomics: data preparation, clustering-based evaluation of the Metabolomic Informative Content and comparison with 1H-NMR. Metabolomics, 11(6), 1756-1768. https://doi.org/10.1007/s11306-015-0830-7 (Original work published 2015)
Bernard, G., Verleysen, M., & Lee, J. (2015). Incremental classification of objects in scenes: Application to the delineation of images. Neurocomputing, 152(1), 45-57. https://doi.org/10.1016/j.neucom.2014.11.020 (Original work published 2015)
Keim, D. A., Munzner, T., Rossi, F., & Verleysen, M. (2015). Bridging Information Visualization with Machine Learning. Dagstuhl Reports, 5(3), 1-27. https://doi.org/10.4230/DagRep.5.3.1 (Original work published 2015)
Monographie
Verleysen, M. (2015). 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2015: Proceedings. Michel Verleysen.
Monographie
Verleysen, M. (2014). 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine learning 2014: Proceedings. Michel Verleysen.
Papier de conférence
Eirola, E., Lendasse, A., Corona, F., & Verleysen, M. (2014). The delta test: The 1-NN estimator as a feature selection criterion. International Joint Conference on Neural Networks (IJCNN). Published. 2014 International Joint Conference on Neural Networks (IJCNN 201), Beijing (China). https://doi.org/10.1109/IJCNN.2014.6889560
Diaz, I., Cuadrado, A. A., Pérez, D., Garcia, F. J., & Verleysen, M. (2014). Interactive Dimensionality Reduction for Visual Analytics. Proceedings of ESANN 2014, 183-188.
Peluffo Ordoñez, D. H., Lee, J., & Verleysen, M. (2014). Recent methods for dimensionality reduction: A brief comparative analysis. Proceedings of ESANN 2014, 189-194.
Lee, J., & Verleysen, M. (2014). Two key properties of dimensionality reduction methods. Proceedings of the 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014), 163-170. https://doi.org/10.1109/CIDM.2014.7008663
Lee, J., Peluffo Ordoñez, D. H., & Verleysen, M. (2014). Multiscale stochastic neighbor embedding: Towards parameter-free dimensionality reduction. Proceedings of ESANN 2014, 177-182.
Gustin, L., Durvaux, F., Kerckhof, S., Standaert, F.-X., & Verleysen, M. (2014). Support Vector Machines for Improved IP Detection with Soft Physical Hash Functions. In Emmanuel Prouff (ed.), Proceedings of the 5th International Workshop on Constructive Side-Channel Analysis and Secure Design (COSADE 2014) (p. p. 112-128). Springer. https://doi.org/10.1007/978-3-319-10175-0_9
Peluffo Ordoñez, D. H., Lee, J., & Verleysen, M. (2014). Generalized kernel framework for unsupervised spectral methods of dimensionality reduction. Proceedings of the 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014), 171-177. https://doi.org/10.1109/CIDM.2014.7008664
Degeest, A., Frénay, B., & Verleysen, M. (2014). Automatic Correction of SVM for Drifted Data Classification. Proceedings de la 14 ème conférence Extraction et Gestion des Connaissances (EGC 2014). Published. 14 ème conférence Extraction et Gestion des Connaissances (EGC 2014), Rennes (France).
Document de travail
Feraud, B., Govaerts, B., Verleysen, M., & de Tullio, P. (2014). Statistical treatment of 2D-NMR COSY spectra: data preparation, clustering-based repeatability evaluation and comparison with 1H-NMR (ISBA Discussion Paper 2014/33).
Chapitre de livre
Lee, J., & Verleysen, M. (2014). Two key properties of dimensionality reduction methods. In Lee, J.A.; Verleysen, M. (ed.), 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (p. p. 163-170).
Article de journal
Bernard, G., Verleysen, M., & Lee, J. (2014). SU-C-18A-03: Automatic Organ at Risk Delineation with Machine Learning Techniques. Medical Physics, 41(6), 101. https://doi.org/10.1118/1.4887830 (Original work published 2014)
Frénay, B., & Verleysen, M. (2014). Pointwise probability reinforcements for robust statistical inference. Neural Networks, 50, 124-141. https://doi.org/10.1016/j.neunet.2013.11.012 (Original work published 2013)
Frénay, B., Doquire, G., & Verleysen, M. (2014). Estimating Mutual information for feature selection in the presence of label noise. Computational Statistics & Data Analysis, 71, 832-848. https://doi.org/10.1016/j.csda.2013.05.001 (Original work published 2013)
Peluffo Ordoñez, D. H., Lee, J., & Verleysen, M. (2014). Short review of dimensionality reduction methods based on stochastic neighbour embedding. Advances in Self-Organizing Maps and Learning Vector Quantization, 295(part I), 65-74. https://doi.org/10.1007/978-3-319-07695-9_6 (Original work published 2014)
Papier de conférence
Schaefer, M., Zhang, L., Schreck, T., Tatu, A., Lee, J., Verleysen, M., & Keim, D. A. (2013). Improving projection-based data analysis by feature space transformations. Proceedings SPIE 8654, Visualization and Data Analysis 2013. Published. Visualization and Data Analysis (VDA 2013), Burlingame, CA (USA). https://doi.org/10.1117/12.2000701
Frénay, B., Doquire, G., & Verleysen, M. (2013). Mutual Information: an Adequate Tool for Feature Selection. Proceedings of the 22nd edition of the annual Belgian-Dutch Conference on Machine Learning (BENELEARN 2013). Published. 22nd edition of the annual Belgian-Dutch Conference on Machine Learning (BENELEARN 2013), Nijmegen (the Netherlands).
Bernard, G., Verleysen, M., & Lee, J. (2013). Segmentation with Incremental Classifiers. In A. Petrosino (ed.), Image Analysis and Processing – ICIAP 2013 (p. p. 81-90). Springer. https://doi.org/10.1007/978-3-642-41184-7_9
Renard, E., Dupont, P., & Verleysen, M. (2013). User control for adjusting conflicting objectives in parameter-dependent visualization of data. Workshop on Visual Analytics using Multidimensional Projections (EuroVis 2013), Leipzig (Germany).
Verleysen, M., & Lee, J. (2013). Nonlinear dimensionality reduction for visualization. Lecture Notes in Computer Science, 8226, 617-622. https://doi.org/10.1007/978-3-642-42054-2_77 (Original work published 2013)
Paul, J., Verleysen, M., & Dupont, P. (2013). Identification of Statistically Significant Features from Random Forests. ECML workshop on Solving Complex Machine Learning Problems with Ensemble Methods, Prague (Czech Republic).
Garcia-Fernandez, F., Verleysen, M., Lee, J., & Diaz, I. (2013). Stability comparison of dimensionality reduction techniques attending to data and parameters variations. Visual Analytics using Multidimensional Projections Workshop (VAMP 2013-EuroVis 2013), Leipzig (Germany).
Garcia Fernandez, F. J., Verleysen, M., Lee, J., & Diaz, I. (2013). Sensitivity to parameter and data variations in dimensionality reduction techniques. Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), p. 95-100.
Doquire, G., Frénay, B., & Verleysen, M. (2013). Risk Estimation and Feature Selection. Proceedings of European Symposium on Artificial Neural Networks (ESANN 2013), 161-166.
Feraud, B., Govaerts, B., Verleysen, M., & et al. (2013). Assessing the repeatability and statistical advantages of homonuclear 2D-NMR spectra: an innovative clustering approach. 9th Annual International Conference of the Metabolomics Society, Glasgow (UK).
Feraud, B., de Tullio, P., Govaerts, B., & Verleysen, M. (2013). Assessing the repeatability and statistical advantages of homonuclear 2D-NMR spectra: a clustering approach. First Belgian-Netherlands Joint Symposium on Metabolomics, Spa (Belgium).
Chapitre de livre
Doquire, G., & Verleysen, M. (2013). A Performance Evaluation of Mutual Estimators for Multivariate Feature Selection. In P.L.Carmona et al. (ed.), Pattern Recognition - Applications and Methods (p. p. 51-63). Springer-Verlag. https://doi.org/10.1007/978-3-642-36530-0_5
Article de journal
Doquire, G., & Verleysen, M. (2013). Mutual information-based feature selection for multilabel classification. Neurocomputing, 122, 148-155. https://doi.org/10.1016/j.neucom.2013.06.035 (Original work published 2013)
Doquire, G., & Verleysen, M. (2013). A graph Laplacian based approach to semi-supervised feature selection for regression problems. Neurocomputing, 121, 5-13. https://doi.org/10.1016/j.neucom.2012.10.028 (Original work published 2013)
Frénay, B., Doquire, G., & Verleysen, M. (2013). Is mutual information adequate for feature selection in regression? Neural Networks, 48, 1-7. https://doi.org/10.1016/j.neunet.2013.07.003 (Original work published 2013)
Frénay, B., Doquire, G., & Verleysen, M. (2013). Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification. Neurocomputing, 112, 64-78. https://doi.org/10.1016/j.neucom.2012.12.051 (Original work published 2013)
Frénay, B., van Heeswijk, M., Miche, Y., Verleysen, M., & Lendasse, A. (2013). Feature selection for nonlinear models with extreme learning machines. Neurocomputing, 102, 111-124. https://doi.org/10.1016/j.neucom.2011.12.055 (Original work published 2013)
de Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., & Blondel, V. (2013). Unique in the Crowd: The privacy bounds of human mobility. Scientific Reports, 3(1376), 1-5. https://doi.org/10.1038/srep01376 (Original work published 2013)
Eirola, E., Doquire, G., Verleysen, M., & Landasse, A. (2013). Distance estimation in numerical data sets with missing values. Information Sciences, 240, 115-128. https://doi.org/10.1016/j.ins.2013.03.043 (Original work published 2013)
Monographie
Verleysen, M. (2013). 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Proceedings. i6doc.com.
Borgelt, C., Gil, M. A., Souza, J. M. C., & Verleysen, M. (2013). Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Springer.
Document de travail
Rousseau, R., Feraud, B., Govaerts, B., & Verleysen, M. (2013). Combination of Independent Component Analysis and statistical modelling for the identification of metabonomic biomarkers in 1H-NMR spectroscopy (second version) (ISBA Discussion Paper 2013/06).
Monographie
Verleysen, M. (2012). 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2012: Proceedings. i6doc.com.
Papier de conférence
Doquire, G., & Verleysen, M. (2012). A Comparison of Multivariate Mutual Information Estimators for Feature Selection. Proceedings of the 2012 International Conference on Pattern Recognition Applications and Methods (ICPRAM 2012), p. 176-185. https://doi.org/10.5220/0003726101760185
Keim, D. A., Rossi, F., Seidl, T., Verleysen, M., & Wrobel, S. (2012). Information Visualization, Visual Data Mining and Machine Learning (Dagstuhl Seminar 12081). Informatik-Spektrum : archive of applied mechanics, 35(4), 311-317. https://doi.org/10.1007/s00287-012-0634-3 (Original work published 2012)
Bernard, G., Lee, J., & Verleysen, M. (2012). Incremental feature computation and classification for image segmentation. Proceedings of the 20th International Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), p. 157-162.
Verleysen, M. (2012). Information theoretic feature selection for high-dimensional data analysis. Proceedings of the Workshop “New Challenges in Neural Computation” (NC2). Published. Workshop “New Challenges in Neural Computation” (NC2), Graz (Austria).
Coelho, F., Braga, A. P., & Verleysen, M. (2012). Cluster homogeneity as a semi-supervised principle for feature selection using mutual information. Proceedings of the 20th International Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), p. 507-512.
Verleysen, M. (2012). About the Optimality of Mutual Information for Feature Selection. Proceedings of the International Conference on Neural Information Processing (ICONIP 2012). Published. International Conference on Neural Information Processing (ICONIP 2012), Doha (Qatar).
Paul, J., Verleysen, M., & Dupont, P. (2012). The stability of feature selection and class prediction from ensemble tree classifiers. ESANN 2012 The 20 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings - Bruges, Belgium from 25 to 27 April 2012 ., 263-268.
Keim, D. A., Rossi, F., Seidl, T., Verleysen, M., & Wrobel, S. (2012). Information Visualization, Visual Data Mining and Machine Learning. In Daniel A.Keim, Fabrice Rossi, Thomas Seidl, Michel Verleysen, Stefan Wrobel (ed.), Dagstuhl Reports (p. p. 58-83). Dagstuhl Publishing. https://doi.org/10.4230/DagRep.2.2.58
Frénay, B., Doquire, G., & Verleysen, M. (2012). On the Potential Inadequacy of Mutual Information for Feature Selection. Proceedings of the 20th International Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), p. 501-506.
Doquire, G., & Verleysen, M. (2012). Handling Imprecise Labels in Feature Selection with Graph Laplacian. Proceedings of the 2012 International Conference on Pattern Recognition Applications and Methods (ICPRAM 2012), p. 162-169. https://doi.org/10.5220/0003712101620169
Article de journal
Doquire, G., & Verleysen, M. (2012). Feature selection with missing data using mutual information estimators. Neurocomputing, 90(1), 3-11. https://doi.org/10.1016/j.neucom.2012.02.031 (Original work published 2012)
de Lannoy, G., François, D., Delbeke, J., & Verleysen, M. (2012). Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Transactions on Biomedical Engineering, 59(1), 241-247. https://doi.org/10.1109/TBME.2011.2171037 (Original work published 2012)
Chapitre de livre
Lee, J., & Verleysen, M. (2012). Graph-Based Dimensionality Reduction. In Olivier Lézoray, Leo Grady (ed.), Image Processing and Analysis with Graphs: Theory and Practice (p. p. 351-382). CRC Press.
Chapitre de livre
François, D., Wertz, V., & Verleysen, M. (2011). Choosing the Metric: A Simple Model Approach. In Norbert Jankowski (ed.), Meta-Learning in Computational Intelligence (p. p. 97-115). Springer. https://doi.org/10.1007/978-3-642-20980-2_3
de Lannoy, G., François, D., Delbeke, J., & Verleysen, M. (2011). Weighted SVMs and Feature Relevance Assessment in Supervised Heart Beat Classification. In Fred Ana (ed.), Biomedical Engineering Systems and Technologies: Third International Joint Conference, BIOSTEC 2010, Valencia, Spain, January 20-23, 2010, Revised Selected Papers (p. p. 212-223). Springer-Verlag. https://doi.org/10.1007/978-3-642-18472-7_17
Papier de conférence
Doquire, G., & Verleysen, M. (2011). Feature Selection for Multi-label Classification Problems. In Joan Cabestany (ed.), Advances in Computational Intelligence (p. p. 9-16). Springer. https://doi.org/10.1007/978-3-642-21501-8_2
Côme, E., Cottrell, M., Verleysen, M., & Lacaille, J. (2011). Aircraft Engine Fleet Monitoring Using Self-Organizing Maps and Edit Distance. In Jorma Laaksonen (ed.), Advances in Self-Organizing maps (p. p. 298-307). Springer. https://doi.org/10.1007/978-3-642-21566-7_30
Doquire, G., & Verleysen, M. (2011). Feature selection with mutual information for uncertain data. Lecture Notes in Computer Science, 6862, 330-341. https://doi.org/10.1007/978-3-642-23544-3_25 (Original work published 2011)
Guerrero-Mosquera, C., Verleysen, M., & Navia Vazquez, A. (2011). Dimensionality Reduction of EEG for Classification using Mutual Information and SVM. Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011), 1-6. https://doi.org/10.1109/MLSP.2011.6064595
Doquire, G., & Verleysen, M. (2011). An hybrid approach to feature selection for mixed categorical and continuous data. International Conference on Knowledge Discovery and Information Retrieval (KDIR 2011), Paris (France).
Doquire, G., & Verleysen, M. (2011). Graph Laplacian for Semi-supervised Feature Selection in Regression Problems. In Joan Cabestany (ed.), Advances in Computational Intelligence (p. p. 248-255). Springer. https://doi.org/10.1007/978-3-642-21501-8_31
Lee, J., & Verleysen, M. (2011). Unsupervised dimensionality reduction:from principal component analysis to modern nonlinear techniques. Proceedings des 43e Journées de Statistiques (JDS 2011). 43e Journées de Statistiques (JDS 2011), Gammarth (Tunisia).
Doquire, G., & Verleysen, M. (2011). Mutual information based feature selection for mixed data. ESANN 2011 Proceedings. 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2011), Bruges (Belgium).
Verleysen, M. (2011). Nonlinear Dimensionality Reduction and Feature Selection. 12th EANN / 7th AIAI Joint Conference 2011, Corfu (Greece).
Verleysen, M. (2011). Data Visualization with Nonlinear Projections. Statistique et Informatique pour les Sciences Humaines et Sociales, Paris (France).
Doquire, G., & Verleysen, M. (2011). Mutual information for feature selection with missing data. Proceedings of the 19th European Symposium on Artificial Neural networks, Computational Intelligence and Machine learning (ESANN 2011), 263-268.
Doquire, G., & Verleysen, M. (2011). Mutual information for feature selection with missing data. European Symposium on Artificial Neural Networks (ESANN 2011), Bruges.
Verleysen, M. (2011). Feature selection for high-dimensional data analysis. 2011 International Conference on Neural Computation Theory and Applications (NCTA 2011), Paris (France).
Doquire, G., de Lannoy, G., François, D., & Verleysen, M. (2011). Feature selection for supervised inter-patient heart beat classification. Proceedings of the 4th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2011), 67-73.
Verleysen, M. (2011). Information theoretic feature selection for non-standard data. STATLEARN 2011, Challenging problems in Statistical Learning, Grenoble (France).
Lee, J., & Verleysen, M. (2011). Shift-invariant similarities circumvent distance concentration in stochastic neighbor embedding and variants. Procedia Computer Science, 4, 538-547. https://doi.org/10.1016/j.procs.2011.04.056 (Original work published 2011)
de Lannoy, G., François, D., & Verleysen, M. (2011). Class-Specific Feature Selection for One-Against-All Multiclass SVMs. ESANN 2011 Proceedings, p. 269-274.
Frénay, B., de Lannoy, G., & Verleysen, M. (2011). Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs. Lecture Notes in Computer Science, 6911, 455-470. https://doi.org/10.1007/978-3-642-23780-5 (Original work published 2011)
Verleysen, M. (2011). High-dimensional data analysis: Looking for fast models ? Proceedings of the International Symposium on Extreme Learning Machines (ELM 2011). International Symposium on Extreme Learning Machines (ELM 2011), Hangzhou (China).
Hazan, A., Verleysen, M., Cottrell, M., & Lacaille, J. (2011). Bayesian inference for outlier detection in vibration spectra with small learning dataset. Proceedings of Surveillance 6, 2011, 1-15.
Verleysen, M. (2011). Machine learning for high-dimensional data: the curse of dimensionality, feature selection and manifold learning. Proceedings of the Computational Intelligence in Healthcare summer school (CIHC 2010). Computational Intelligence in Healthcare summer school (CIHC 2011), Eindhoven (The Netherlands).
Article de journal
De Decker, A., François, D., Verleysen, M., & Lee, J. (2011). Mode estimation in high-dimensional spaces with flat-top kernels: Application to image denoising. Neurocomputing, 74(9), 1402-1410. https://doi.org/10.1016/j.neucom.2010.12.013 (Original work published 2011)
Doquire, G., de Lannoy, G., François, D., & Verleysen, M. (2011). Feature selection for supervised inter-patient heart beat classification. Computational Intelligence and Neuroscience, 2011(643816), 1-9. https://doi.org/10.1155/2011/643816 (Original work published 2011)
Frénay, B., & Verleysen, M. (2011). Parameter-insensitive kernel in extreme learning for non-linear support vector regression. Neurocomputing, 74(16), 2526-2531. https://doi.org/10.1016/j.neucom.2010.11.037 (Original work published 2011)
Monographie
Verleysen, M. (2011). ESANN 2011, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2011: Proceedings. i6doc.com.
Papier de conférence
de Lannoy, G., François, D., Delbeke, J., & Verleysen, M. (2010). Feature relevance assessment in automatic inter-patient heart beat classification. Proceedings of the 3rd International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2010), p. 13-20.
Lee, J., & Verleysen, M. (2010). Unsupervised Dimensionality Reduction: Overview and Recent Advances. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), p. 4163-4170.
Lee, J., & Verleysen, M. (2010). Unsupervised dimensionality reduction: from principal component analysis to modern nonlinear techniques. Proceedings of ERCIM working group on Computing & Statistics (ERCIM 2010). ERCIM working group on Computing & Statistics (ERCIM 2010), Senate House, University of London & LSE (United Kingdom).
Miché, Y., Eirola, E., Bas, P., Simula, O., Jutten, C., Lendasse, A., & Verleysen, M. (2010). Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning (ESANN 2010), p. 19-24.
Coelho, F., Braga, A. P., & Verleysen, M. (2010). Multi-Objective Semi-supervised Feature Selection and Model Selection based on Pearson’s Correlation Coefficient. Lecture Notes in Computer Science, 6419, 509-516. (Original work published 2010)
Lee, J., & Verleysen, M. (2010). On the Role and Impact of the Metaparameters in t-distributed Stochastic Neighbor Embedding. Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT 2010). 19th International Conference on Computational Statistics (COMPSTAT 2010), Paris (France).
Co circ me, E., Cottrell, M., Verleysen, M., & Lacaille, J. (2010). Aircraft Engine Health Monitoring Using Self-organizing Maps. In Perner, P.; (ed.), Advances in Data Mining Applications and Theoretical Aspects. 10th Industrial Conference, ICDM 2010 (p. p. 405-417). Springer verlag. https://doi.org/10.1007/978-3-642-14400-4_31
Hazan, A., Verleysen, M., Cottrell, M., & Lacaille, J. (2010). Trajectory Clustering for Vibration Detection in Aircraft Engines. Lecture Notes in Computer Science, 6171, 362-375. (Original work published 2010)
Côme, E., Cottrell, M., Verleysen, M., & Lacaille, J. (2010). Self Organizing Star (SOS) for health monitoring. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning (ESANN 2010), p. 99-104.
Guerrero-Mosquera, C., Verleysen, M., & Navia Vazquez, A. (2010). EEG Feature Selection Using Mutual Information and Support Vector Machine: A Comparative Analysis. Proceedings of the 32nd Annual International IEEE EMBC Conference (EMBC 2010), p. 4946-4949.
Frénay, B., & Verleysen, M. (2010). Using SVMs with randomised feature spaces: an extreme learning approach. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning (ESANN 2010), p. 315-320.
Hazan, A., Verleysen, M., Cottrell, M., & Lacaille, J. (2010). Linear smoothing of FRF for aicraft engine vibration monitoring. Proceedings of the International Conference on Noise and vibration Engineering (ISMA 2010), p. 2857-2868.
Wismueller, A., Verleysen, M., Aupetit, M., & Lee, J. (2010). Recent Advances in Nonlinear Dimensionality Reduction, Manifold and Topological Learning. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning (ESANN 2010), p. 71-80.
Verleysen, M., & Lee, J. (2010). Nonlinear dimensionality reduction. Proceedings of the 11th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2010). 11th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2010), Paisley (Scotland, UK).
Verleysen, M. (2010). Machine learning for high-dimensional data. Proceedings of Artificial Intelligence and Applications (AIA 2010). Artificial Intelligence and Applications (AIA 2010), Innsbruck (Austria).
Onclinx, V., Lee, J., Wertz, V., & Verleysen, M. (2010). Dimensionality reduction by rank preservation. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN 2010), 1599-1606. https://doi.org/10.1109/IJCNN.2010.5596347
De Decker, A., Lee, J., François, D., & Verleysen, M. (2010). Mode Estimation in High-dimensional Spaces with Flat-top Kernels: Application to Image Denoising. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning (ESANN 2010), p. 411-416.
Monographie
Verleysen, M. (2010). 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2010: Proceedings. d-side publ.
Article de journal
Thomas, I., Frankhauser, P., Frénay, B., & Verleysen, M. (2010). Clustering patterns of urban built-up areas with curves of fractal scaling behaviour. Environment and Planning B. Planning and Design, 37(5), 942-954. https://doi.org/10.1068/b36039 (Original work published 2010)
De Decker, A., Lee, J., & Verleysen, M. (2010). A principled approach to image denoising with similarity kernels involving patches. Neurocomputing, 73(7-9), 1199-1209. https://doi.org/10.1016/j.neucom.2009.12.022 (Original work published 2010)
Lee, J., & Verleysen, M. (2010). Scale-independent quality criteria for dimensionality reduction. Pattern Recognition Letters, 31(14), 2248-2257. https://doi.org/10.1016/j.patrec.2010.04.013 (Original work published 2010)
Papier de conférence
De Decker, A., Lee, J., & Verleysen, M. (2009). Variance Stabilizing Transformations in Patch-Based Bilateral Filters for Poisson Noise Image Denoising. Proceedings of EMBC 2009, International Conference of the IEEE Engineering in Medicine and Biology Society, p. 3673-3676.
Frénay, B., de Lannoy, G., & Verleysen, M. (2009). Improving the transition modelling in hidden Markov models for ECG segmentation. Proceedings of the 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009), p. 141-146.
Krier, C., François, D., Rossi, F., & Verleysen, M. (2009). Supervised Variable Clustering for Classification of NIR Spectra. Proceedings of the 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009), p. 263-268.
Verleysen, M. (2009). Feature selection. Soft Computing and Statistics, COST Action IC0702 summer course 2009, Lisbon (Portugal).
Lee, J. A., De Decker, A., & Verleysen, M. (2009). Adaptive anisotropic denoising: a bootstrapped procedure. Proceedings of the 17th European Symposium on Artificail Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009), p. 101-106.
Verleysen, M., & Lee, J. (2009). Nonlinear dimensionality reduction. UCL Large Graphs group seminar, Louvain-la-Neuve (Belgium).
Verleysen, M., & Lee, J. (2009). How to assess the quality of nonlinear dimensionality reduction techniques? Dagstuhl seminar “Similarity-based learning on structures”, Dagstuhl (Germany).
Verleysen, M. (2009). A tribute to Jeanny Hérault. Machine Learning for Signal Processing (MLSP 2009), Grenoble (France).
Cottrell, M., Gaubert, P., Eloy, C., François, D., Hallaux, G., Lacaille, J., & Verleysen, M. (2009). Fault prediction in aircraft engines using self-organizing maps. In Principe, J.C.; Miikkulainen, R. (ed.), Advances in Self-organizing Maps (p. p. 37-44). Springer-verlag. https://doi.org/10.1007/978-3-642-02397-2_5
De Decker, A., Lee, J., & Verleysen, M. (2009). Performance assessment of patch-based bilateral denoising. In Djemal, K. (ed.), First International Workshop on Medical Image Analysis and Description for Diagnosis Systems - MIAD 2009 (p. p. 52-61). Insticc press.
Verleysen, M., & Lee, J. (2009). Nonlinear dimensionality reduction and visualization. SFC (Société Française de Classification) 2009, Grenoble (France).
de Lannoy, G., Verleysen, M., & Delbeke, J. (2009). Assessment and comparison of time realignment methods for supervised heart beat classification. BIOSIGNALS 2009. Second International Conference on Bio-inspired Systems and Signal Processing, p. 239-244.
Azan, A., Verleysen, M., & Cottrell, M. (2009). Random model of vibrations for Foreign Object Damage detection in a civil aircraft engine. Proceedings of MLA 2009, Machine Learning for Aerospace International Workshop, p. 149-152.
Lee, J., & Verleysen, M. (2009). The intrusion-extrusion compromise for the projection and visualization of high-dimensional data. Colloquium “Statistiques pour le traitement de l’image” (STATIM 2009), Université Paris 1 Panthéon-Sorbonne (France).
Frankhauser, P., Frénay, B., Thomas, I., & Verleysen, M. (2009). Clustering patterns of urban builtup areas with curves of fractal scaling behavior. ASRDLF 2009, Association de Science Régionale de Langue Française, Clermont-Ferrand (France).
Thomas, I., Frankhauser, P., Frénay, B., & Verleysen, M. (2009). Clustering fractal urban patterns with curves of scaling behavior. Proceedings of the 49th Congress of the European Regional Science Association “Territorial cohesion of Europe and integrative planning” (ERSA 2009), 71.
de Lannoy, G., Costecalde, T., Marin, J., Verleysen, M., & Delbeke, J. (2009). Elimination of electrocardiogram contamination from vagus nerve recordings using ICA. Proceedings of the 14th Annual International FES Society Conference (IFESS 2009), p. 109-111.
Lee, J., & Verleysen, M. (2009). Simbed: similarity-based embedding. In Alippi, C.; Polycarpou, M.; Ellinas, G.; Panayiotou, C.; (ed.), Lecture Notes in Computer Science (pp. 95-104). Springer verlag. https://doi.org/10.1007/978-3-642-04277-5_10
De Decker, A., Lee, J., & Verleysen, M. (2009). Patch-Based Bilateral Filter and Local M-Smoother for Image Denoising. Proceedings of the 17th European Symposium on Artifician Neural Networks - Advances in Computational Intellignece and Learning (ESANN 2009), p. 95-100.
Chapitre de livre
de Lannoy, G., De Decker, A., & Verleysen, M. (2009). A Supervised Wavelet Transform Algorithm for R Spike Detection in Noisy ECGs. In Ana Fred (ed.), Biomedical Engineering Systems and Technologies (p. p. 256-264). Springer.
Verleysen, M., Rossi, F., & François, D. (2009). Advances in Feature Selection with Mutual Information. In Thomas Villmann (ed.), Similarity-Based Clustering (p. p. 52-69). Springer.
Monographie
Verleysen, M. (2009). 17th European Symposium On Artificial Neural Networks - Advances in Computational Intelligence and Learning 2009 : Proceedings. d-side publ.
Verleysen, M. (2009). Similarity-Based Clustering - Recent Developments and Biomedical Applications. Thomas Villmann, Michael Biehl, Barbara Hammer, Michel Verleysen.
Article de journal
Garcia-Laencina, P. J., Sancho-Gomez, J.-L., Figueiras-Vidal, A. R., & Verleysen, M. (2009). K nearest neighbours with mutual information for simultaneous classification and missing data imputation. Neurocomputing, 72(7-9), 1483-1493. https://doi.org/10.1016/j.neucom.2008.11.026 (Original work published 2009)
Gomez-Verdejo, V., Verleysen, M., & Fleury, J. (2009). Information-theoretic feature selection for functional data classification. Neurocomputing, 72(16-18), 3580-3589. https://doi.org/10.1016/j.neucom.2008.12.035 (Original work published 2009)
Lee, J., & Verleysen, M. (2009). Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing, 72(7-9), 1431-1443. https://doi.org/10.1016/j.neucom.2008.12.017 (Original work published 2009)
Liitiainen, E., Verleysen, M., Corona, F., & Lendasse, A. (2009). Residual variance estimation in machine learning. Neurocomputing, 72(16-18), 3692-3703. https://doi.org/10.1016/j.neucom.2009.07.004 (Original work published 2009)
Onclinx, V., Wertz, V., & Verleysen, M. (2009). Nonlinear data projection on non-Euclidean manifolds with controlled trade-off between trustworthiness and continuity. Neurocomputing, 72(7-9), 1444-1454. https://doi.org/10.1016/j.neucom.2008.12.018 (Original work published 2009)
Papier de conférence
Onclinx, V., Wertz, V., & Verleysen, M. (2008). Nonlinear data projection on a sphere with a controlled trade-off between trustworthiness and continuity. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), p. 43-48.
Verleysen, M., & François, D. (2008). Parameter-free feature selection with mutual information. Proceedings of the first workshop of the ERCIM Working Group on Computing and Statistics, p. 13.
Rui Nian, Guangrong Ji, & Verleysen, M. (2008). An unsupervised Gaussian mixture classification mechanism based on statistical learning analysis. Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2008), 14-18. https://doi.org/10.1109/FSKD.2008.333
Lee, J., & Verleysen, M. (2008). Quality Assessment of nonlinear dimensionality reduction based on K-ary neighborhood. Journal of Machine Learning Research: Workshop and Conference proceedings, 4, 21-35. (Original work published 2008)
Rui Nian, Guangrong Ji, & Verleysen, M. (2008). An Alternative to Center-based Clustering Algorithm via Statistical Learning Analysis. Advanced Intelligent Computing Theories and Applications With Aspects of Artificial Intelligence., p. 693-700. https://doi.org/10.1007/978-3-540-85984-0_83
de Lannoy, G., Frénay, B., Verleysen, M., & Delbeke, J. (2008). Supervised ECG Delineation Using the Wavelet Transform and Hidden Markov Models. In Vander Sloten, J.; Nyssen, M.; Verdonck, P.; Haueisen, J.; (ed.), Proceedings of the 4th European Conference of the International Federation for Medical and Biological Engineering (MBEC 2008) (pp. 22-25). Springer verlag.
Delannay, N., Archambeau, C., & Verleysen, M. (2008). Improving the robustness to outliers of mixtures of probabilistic PCAs. Advances in Knowledge Discovery and Data Mining, p. 527-535. https://doi.org/10.1007/978-3-540-68125-0_47
Lee, J., & Verleysen, M. (2008). Rank-based quality assessment of nonlinear dimensionality reduction. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), p. 49-54.
Verleysen, M. (2008). High-dimensional Data Analysis and Feature Selection. EVIC (Latin-American Summer School on Computational Intelligence), Santiago (Chile).
Verleysen, M. (2008). Nonlinear projection. EVIC (Latin-American Summer School on Computational Intelligence) 2008, Santiago (Chile).
Verleysen, M. (2008). Feature selection with low-dimensional mutual information. Proceedings of the 8th International Conference on Operations Research (OrHavana 2008). 8th International Conference on Operations Research (OrHavana 2008), Havana (Cuba).
François, D., Krier, C., Rossi, F., & Verleysen, M. (2008). Estimation de redondance pour le clustering de variables spectrales. Proceedings of the 10th European Symposium on Statistical Methods for the Food Industry (AGROSTAT 2008), p. 55-61.
Frénay, B., de Lannoy, G., & Verleysen, M. (2008). Emission Modelling for Supervised ECG Segmentation using Finite Differences. In Vander Sloten, J.; Nyssen, M.; Verdonck, P.; Haueisen, J.; (ed.), Proceedings of the 4th European Conference of the International Federation for Medical and Biological Engineering - MBEC 2008 (p. p. 1212-1216). Springer verlag.
de Lannoy, G., De Decker, A., & Verleysen, M. (2008). A supervised learning approach based on the continuous wavelet transform for R spike detection in ECG. Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2008), 140-145.
Onclinx, V., Verleysen, M., & Wertz, V. (2008). Projection of time series with periodicity on a sphere. Proceedings of the European Symposium on Time Series Prediction (ESTSP′08), p. 47-56.
Eirola, E., Liitiäinen, E., Lendasse, A., Corona, F., & Verleysen, M. (2008). Using the Delta Test for Variable Selection. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), p. 25-30.
Garcia-Laencina, P., Sancho-Gomez, J.-L., Figueiras-Vidal, A. R., & Verleysen, M. (2008). K-nearest neighbours based on mutual information for incomplete data classification. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), p. 25-30.
Chapitre de livre
Biga Diambeidou, M., François, D., Gailly, B., Verleysen, M., & Wertz, V. (2008). An empirical taxonomy of start-up firms growth trajectories. In Fayolle A. et Kyro P. (ed.), The Dynamics between Entrepreneurship, Environment and Education (p. p. 193-220). Edward Elgar.
Diambeidou, M. B., Wertz, V., Verleysen, M., Gailly, B., & François, D. (2008). Empirical Taxonomy of Start-Up Firms Growth Trajectories. In Fayolle, Alain (ed.) (ed.), The Dynamics Between Entrepreneurship, Environment and Education.
Article de journal
Krier, C., Rossi, F., François, D., & Verleysen, M. (2008). A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis. Chemometrics and Intelligent Laboratory Systems, 91(1), 43-53. https://doi.org/10.1016/j.chemolab.2007.09.004 (Original work published 2008)
Assenza, A., Valle, M., & Verleysen, M. (2008). A Comparative Study of Various Probability Density Estimation Methods for Data Analysis. International Journal of Computational Intelligence Systems, 1(2), 188-201. https://doi.org/10.1080/18756891.2008.9727616 (Original work published 2008)
de Lannoy, G., Marin, J., Verleysen, M., & Delbeke, J. (2008). Filtering Heart Related Activity from Vagus Nerve Recordings in Rats. Biomedical Technology.
Archambeau, C., Delannay, N., & Verleysen, M. (2008). Mixtures of robust probabilistic principal component analyzers. Neurocomputing, 71(7-9), 1274-1282. https://doi.org/10.1016/j.neucom.2007.11.029 (Original work published 2008)
Delannay, N., & Verleysen, M. (2008). Collaborative filtering with interlaced generalized linear models. Neurocomputing, 71(7-9), 1300-1310. https://doi.org/10.1016/j.neucom.2007.12.021 (Original work published 2008)
Lee, J., Vrins, F., & Verleysen, M. (2008). Blind source separation based on endpoint estimation with application to the MLSP 2006 data competition. Neurocomputing, 72(1-3), 47-56. https://doi.org/10.1016/j.neucom.2007.12.047 (Original work published 2008)
Pham, D.-T., Vrins, F., & Verleysen, M. (2008). On the risk of using Renyi’s entropy for blind source separation. IEEE Transactions on Signal Processing, 56(10), 4611-4620. https://doi.org/10.1109/TSP.2008.928109 (Original work published 2008)
Mujica, L. E., Vehi, J., Ruiz, M., Verleysen, M., Staszewski, W., & Worden, K. (2008). Multivariate statistics process control for dimensionality reduction in structural assessment. Mechanical Systems and Signal Processing, 22(1), 155-171. https://doi.org/10.1016/j.ymssp.2007.05.001 (Original work published 2008)
Rousseau, R., Govaerts, B., Verleysen, M., & Boulanger, B. (2008). Comparison of some chemometric tools for metabonomics biomarker identification. Chemometrics and Intelligent Laboratory Systems, 91(1), 54-66. https://doi.org/10.1016/j.chemolab.2007.06.008 (Original work published 2008)
Document de travail
Rousseau, R., Govaerts, B., & Verleysen, M. (2008). Combination of Independent Component Analysis and statistical modelling for the identification of metabonomic biomarkers in 1H-NMR spectroscopy (STAT Discussion Paper 0941).
Monographie
Verleysen, M. (2008). 16th European Symposium On Artificial Neural Networks - Advances in Computational Intelligence and Learning 2008: Proceedings. d-side publ.
Papier de conférence
Krier, C., François, D., Rossi, F., & Verleysen, M. (2007). Feature clustering and mutual information for the selection of variables in spectral data. Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2007), p. 157-162.
De Decker, A., de Lannoy, G., & Verleysen, M. (2007). Functional SOM for variable-length signal windows. Proceedings of the 6th International Workshop on Self-Organizing Maps (WSOM 2007), p. 6 pages.
Diambeidou, M. B., Verleysen, M., & Gailly, B. (2007). Une Taxonomie des Trajectoires de Croissance Initiale des jeunes Entreprises. Proceedings du Vème Congrès International de l’Académie de l’Entrepreneuriat, 27 pages.
Dablemont, S., Van Bellegem, S., & Verleysen, M. (2007). Forecasting high and low of financial time series by particle filters and Kalman filters.
Diambeidou, M. B., Wertz, V., Verleysen, M., Gailly, B., & François, D. (2007). An Empirical Taxonomy of Start-Up Firms Growth Trajectories. The OECD Entrepreneurship Indicators Programme : Workshop on the Measurement of High-Growth Enterprises, 28. (Original work published 2007)
François, D., Krier, C., Rossi, F., & Verleysen, M. (2007). Estimation de redondance conditionnelle par information mutuelle, application au clustering de variables spectrales. Proceedings de Chimiométrie 2007, p. 43-46.
Verleysen, M. (2007). Independent Component Analysis and Nonlinear Projections. Artificial Perception doctoral school, Universidad del Pais Vasco (Spain).
Delannay, N., & Verleysen, M. (2007). Collaborative filtering with interlaced Generalized Linear Models. Proceedings of the 2007 European Symposium on Artificial Neural Networks (ESANN 2007), p. 247-252.
Verleysen, M. (2007). Feature selection with mutual information and resampling. Dagstuhl seminar “Similary-based clustering and its application to medicine and biology”, Dagstuhl (Germany).
Gomez-Verdejo, V., Verleysen, M., & Fleury, J. (2007). Information-theoretic feature selection for the classification of hysteresis curves. In Francisco Sandoval et al. eds. (ed.), International Work-Conference on Artificial Neural Networks (IWANN ’07) (pp. 522-529). Springer-Verlag.
Verleysen, M. (2007). High-dimensional data. Artificial Perception doctoral school, Universidad del Pais Vasco (Spain).
Dablemont, S., Van Bellegem, S., & Verleysen, M. (2007). Modelling and Forecasting financial time series of “tick data” by functional analysis and neural networks. Proceedings of Forecasting Financial Markets 2007, p. 1-18.
Verleysen, M. (2007). Learning High-Dimensional Data with Artificial Neural Networks.
Archambeau, C., Delannay, N., & Verleysen, M. (2007). Mixtures of robust probabilistic principal component analyzers. Proceedings of the 2007 European Symposium on Artificial Neural Networks (ESANN 2007), p. 229-234.
Verleysen, M. (2007). Sélection de variables par information mutuelle et rééchantillonnage. SAMOS-MATISSE-CES, Université Paris 1 Panthéon-Sorbonne (France).
Verleysen, M., & Archambeau, C. (2007). PCA and Mixtures of PCA: Improving the robustness to outliers. SAMOS-MATISSE-CES, Université Paris 1 Panthéon-Sorbonne (France).
Verleysen, M. (2007). Time series prediction and the curse of dimensionality. European Symposium on Time Series Prediction (ESTSP 2007), Helsinki (Finland).
Monographie
Lee, J., & Verleysen, M. (2007). Nonlinear dimensionality reduction. Springer.
Verleysen, M. (2007). 15th European Symposium On Artificial Neural Networks - Advances in Computational Intelligence and Learning 2007: Proceedings. d-side publ.
Article de journal
Vrins, F., Pham, D.-T., & Verleysen, M. (2007). Mixing and non-mixing local minima of the entropy contrast for blind source separation. IEEE Transactions on Information Theory, 53(3), 1030-1042. https://doi.org/10.1109/TIT.2006.890716 (Original work published 2007)
Simon, G., Lee, J., Cottrell, M., & Verleysen, M. (2007). Forecasting the CATS benchmark with the Double Vector Quantization method. Neurocomputing, 70(13-15), 2400-2409. https://doi.org/10.1016/j.neucom.2005.12.137 (Original work published 2007)
Archambeau, C., & Verleysen, M. (2007). Robust Bayesian clustering. Neural Networks, 20(1), 129-138. https://doi.org/10.1016/j.neunet.2006.06.009 (Original work published 2007)
Simon, G., & Verleysen, M. (2007). High-dimensional delay selection for regression models with mutual information and distance-to-diagonal criteria. Neurocomputing, 70(7-9), 1265-1275. https://doi.org/10.1016/j.neucom.2006.10.150 (Original work published 2007)
Lespinats, S., Verleysen, M., Giron, A., & Fertil, B. (2007). DD-HDS: A method for visualization and exploration of high-dimensional data. IEEE Transactions on Neural Networks, 18(5), 1265-1279. https://doi.org/10.1109/TNN.2007.891682 (Original work published 2007)
François, D., Wertz, V., & Verleysen, M. (2007). The concentration of fractional distances. IEEE Transactions on Knowledge & Data Engineering, 19(7), 873-886. https://doi.org/10.1109/TKDE.2007.1037 (Original work published 2007)
François, D., Rossi, F., Wertz, V., & Verleysen, M. (2007). Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing, 70(7-9), 1265-1275. https://doi.org/10.1016/j.neucom.2006.11.019 (Original work published 2007)
Biga Diambeidou, M., Wertz, V., Verleysen, M., Janssen, F., Gailly, B., & François, D. (2007). Les trajectoires de croissance des jeunes entreprises. Management & Prospective, 24(3), 83-102. (Original work published 2007)
Caetano, S., Krier, C., Verleysen, M., & Heyden, Y. V. (2007). Modelling the quality of enantiomeric separations using Mutual Information as an alternative variable selection technique. Analytica Chimica Acta, 602(1), 37-46. https://doi.org/10.1016/j.aca.2007.08.048 (Original work published 2007)
Rossi, F., François, D., Wertz, V., Meurens, M., & Verleysen, M. (2007). Fast selection of spectral variables with B-spline compression. Chemometrics and Intelligent Laboratory Systems, 86(2), 208-218. https://doi.org/10.1016/j.chemolab.2006.06.007 (Original work published 2007)
Lendasse, A., Oja, E., Simula, O., & Verleysen, M. (2007). Time series prediction competition: The CATS benchmark. Neurocomputing, 70(13-15), 2325-2329. https://doi.org/10.1016/j.neucom.2007.02.013 (Original work published 2007)
Vrins, F., Lee, J., & Verleysen, M. (2007). A minimum-range approach to blind extraction of bounded sources. IEEE Transactions on Neural Networks, 18(3), 809-822. https://doi.org/10.1109/TNN.2006.889941 (Original work published 2007)
Chapitre de livre
Vrins, F., Dinh-Tuan Pham, & Verleysen, M. (2007). Is the general form of Renyi’s entropy a contrast for source separation? In Davies, M.E.; James, C.; Davies, M.; Abdallah, S.; Plumbley, M. (ed.), Independent Component Analysis and Signal Separation, ICA 2007 (pp. 129-136). Springer.
Document de travail
Dablemont, S., Van Bellegem, S., & Verleysen, M. (2007). Modelling and Forecasting financial time series of «tick data» by functional analysis and neural networks.
Rousseau, R., Govaerts, B., Verleysen, M., & Boulanger, B. (2007). Comparison of some chemometric tools for metabonomics biomarker identification (STAT Discussion Paper 0711).
Papier de conférence
Dablemont, S., Verleysen, M., & Van Bellegem, S. (2006). Modelling and Forecasting financial time series of “tick data”. Proceedings of the 26th International Sysmposium on Forecasting (ISF 2006), p. 60.
Krier, C., François, D., Wertz, V., & Verleysen, M. (2006). Feature Scoring by Mutual Information for Classification of Mass Spectra. Proceedings of the 7th International FLINS Conference on Applied Artificial Intelligence (FLINS 2006), 557-564.
Rossi, F., François, D., Wertz, V., & Verleysen, M. (2006). A functional approach to variable selection in spectrometric problems. Lecture Notes in Computer Science, 4131, 11-20. https://doi.org/10.1007/11840817_2 (Original work published 2006)
Simon, G., & Verleysen, M. (2006). Lag selection for regression models using high-dimensional mutual information. Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN 2006), 395-400.
Delannay, N., Archambeau, C., & Verleysen, M. (2006). Automatic adjustment of discriminant adaptive nearest neighbor. In Tang, Y.Y.; Wang, S.P.; Lorette, G.L.; Yeung, D.S.; Yan, H.; (ed.), Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006) (p. 4 pages). IEEE comput. soc.
Caetano, S., Krier, C., Verleysen, M., & Vander Heyden, Y. (2006). Mutual Information for the selection of variables to model enantioselectivity. Proceedings of the 4th International Chemometrics Research Symposium (ICRM 2006). 4th International Chemometrics Research Symposium (ICRM 2006), Veldhoven (the Netherlands).
Biga Diambeidou, M., Wertz, V., Verleysen, M., Gailly, B., & François, D. (2006). Empirical Taxonomy of Start-Up Firms Growth Trajectories. Proceedings of the European Summer University Conference on Entrepreneurship and Entrepreneurship Education Research (ESUCO 2006), 506-530.
Archambeau, C., Delannay, N., & Verleysen, M. (2006). Robust Probabilistic Projections. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), p. 33-40.
Lee, J., Vrins, F., & Verleysen, M. (2006). A least absolute bound approach to ICA: application to the MLSP 2006 competition. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 41-46.
Sameni, R., Vrins, F., Parmentier, F., Vigneron, V., Verleysen, M., Jutten, C., Shamsollahi, M. B., & Hérail, C. (2006). Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria. Proceedings of MaxEnt 2006. 26th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2006), CNRS (Paris/France).
François, D., Wertz, V., & Verleysen, M. (2006). The permutation test for feature selection by mutual information. Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN 2006), p. 239-244.
Lendasse, A., Corona, F., Hao, J., Reyhani, N., & Verleysen, M. (2006). Determination of the Mahalanobis matrix using nonparametric noise estimations. Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN 2006), p. 227-232.
Caetano, S., Krier, C., Verleysen, M., & Vander Heyden, Y. (2006). Modélisation de la qualité des séparations énantiomériques utilisant le critère d’information mutuelle. Proceedings de Chimiométrie 2006, p. 170-173.
Herrera, L. J., Pomares, H., Rojas, I., Verleysen, M., & Guilen, A. (2006). Effective input variable selection for function approximation. Lecture Notes in Computer Science, 4131, 41-50. https://doi.org/10.1007/11840817_5 (Original work published 2006)
Krier, C., François, D., & Verleysen, M. (2006). Une approche orientée données pour la projection de variables spectrales en spectrométrie. Proceedings de Chimiométrie 2006, p. 53-59.
Lee, J., Vrins, F., & Verleysen, M. (2006). Non-Orthogonal Support-Width ICA. Proceedings the 14th European Symposium on Artificial Neural Networks (ESANN 2006), p. 351-358.
Archambeau, C., Valle, M., Assenza, A., & Verleysen, M. (2006). Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS 2006), 3245-3248.
Chapitre de livre
Vrins, F., & Verleysen, M. (2006). Minimum support ICA using order statistics. Part II: Performance analysis. In J. Rosca, D. Erdogmus, J. Principe and S. Haykin (ed.), Independent Component Analysis and Blind Signal Sepration, ICA 2006 (pp. 270-277). Springer-Verlag. https://doi.org/10.1007/11679363_34
Vrins, F., Erdogmus, D., Jutten, C., & Verleysen, M. (2006). Zero-entropy minimization for blind extraction of bounded sources (BEBS). In J. Rosca, D. Erdogmus, J. Principe and S. Haykin (ed.), Independent Component Analysis and Blind Signal Sepration, ICA 2006 (pp. 747-754). Springer-Verlag. https://doi.org/10.1007/11679363_93
Vrins, F., & Verleysen, M. (2006). Minimum support ICA using order statistics. Part I: Quasi-range based support estimation. In J. Rosca, D. Erdogmus, J. Principe and S. Haykin (ed.), Independent Component Analysis and Blind Signal Sepration, ICA 2006 (pp. 262-269). Springer-Verlag. https://doi.org/10.1007/11679363_33
Article de journal
Cottrell, M., & Verleysen, M. (2006). Advances in self-organizing maps - Introduction. Neural Networks, 19(6-7), 721-722. https://doi.org/10.1016/j.neunet.2006.05.011 (Original work published 2006)
Rossi, F., Lendasse, A., François, D., Wertz, V., & Verleysen, M. (2006). Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemometrics and Intelligent Laboratory Systems, 80(2), 215-226. https://doi.org/10.1016/j.chemolab.2005.06.010 (Original work published 2006)
Simon, G., Lee, J., & Verleysen, M. (2006). Unfolding preprocessing for meaningful time series clustering. Neural Networks, 19(6-7), 877-888. https://doi.org/10.1016/j.neunet.2006.05.020 (Original work published 2006)
Monographie
Verleysen, M. (2006). 14th European Symposium on Artificial Neural Networks 2006: Proceedings. d-side publ.
Verleysen, M. (2006). Neural Networks special issue on “Advances in Self-Organizing Maps” (Vol. 19, Nos. 6-7 (July-August 2006)). M. Cottrell, M. Verleysen.
Papier de conférence
Sorjamaa, A., Lendasse, A., & Verleysen, M. (2005). Pruned Lazy Learning Models for Time Series Prediction. Proceedings of ESANN 2005, European Symposium on Artificial Neural Networks, p. 509-514.
Vrins, F., Verleysen, M., & Jutten, C. (2005). SWM : a class of convex contrasts for source separation. IEEE International Conference on Acoustics, Speech, and SignalProcessing (ICASSP 2005), Vol. 5, p. 16:1-4.
Rossi, F., Delannay, N., Conan-Guez, B., & Verleysen, M. (2005). Representation of functional data in neural networks. Neurocomputing, 64, 183-210. https://doi.org/10.1016/j.neucom.2004.11.012 (Original work published 2005)
François, D., Wertz, V., & Verleysen, M. (2005). Non Euclidean metrics for similarity search in noisy datasets. Proceedings of ESANN 2005, 13h European Symposium on Artificial Neural Networks, p. 339-344.
Lendasse, A., François, D., Wertz, V., & Verleysen, M. (2005). Estimation non paramétrique de bruit pour la construction de modèles non linéaires en spectrométrie. Proceedings de Chimiométrie 2005, p. 143-146.
de Marneffe, M.-C., Archambeau, C., Dupont, P., & Verleysen, M. (2005). Local Vector-based Models for Sense Discrimination. Proceedings of IWCS 2005, 6th International Workshop on Computational Semantics, Tilburg (the Netherlands).
Archambeau, C., & Verleysen, M. (2005). Manifold constrained finite Gaussian mixtures. Lecture Notes in Computer Science, 3512, 820-828. (Original work published 2005)
Lendasse, A., Simon, G., Wertz, V., & Verleysen, M. (2005). Fast bootstrap methodology for regression model selection. Neurocomputing, 64, 161-181. https://doi.org/10.1016/j.neucom.2004.11.017 (Original work published 2005)
Lendasse, A., Ji, J., Reyhani, N., & Verleysen, M. (2005). LS-SVM hyperparameter selection with a nonparametric noise estimator. Lecture Notes in Computer Science, 3697, 625-630. (Original work published 2005)
Simon, G., Lee, J., & Verleysen, M. (2005). On the need of unfolding preprocessing for time series clustering. Proceedings the 5th Workshop on Self-Organizing Maps (WSOM 2005), p. 251-258.
Lee, J., Vrins, F., & Verleysen, M. (2005). A simple ICA algorithm for non-differentiable contrasts. Proceedings of EUSIPCO 2005, 1412: 1-4.
Verleysen, M., & François, D. (2005). The curse of dimensionality in data mining and time series prediction. Lecture Notes in Computer Science, 3512, 758-770. (Original work published 2005)
Rossi, F., François, D., Wertz, V., & Verleysen, M. (2005). Sélection de groupes de variables spectrales par information mutuelle grâce à une représentation spline. Proceedings de Chimiométrie 2005, p. 52-55.
François, D., Wertz, V., & Verleysen, M. (2005). About the locality of kernels in high-dimensional spaces. Proceedings of ASMDA 2005, International Symposium on Applied Stochastic Models and Data Analysis, p. 238-245.
Vrins, F., Lee, J., & Verleysen, M. (2005). Can we always trust entropy minima in the ICA context ? Proceedings of EUSIPCO 2005, 1107: 1-4.
Archambeau, C., & Verleysen, M. (2005). Manifold constrained variational mixtures. Lecture Notes in Computer Science, 3697, 279-284. (Original work published 2005)
Catteau, D., Simon, G., Ben Omrane, W., & Verleysen, M. (2005). Using the Self-Organizing Maps to prove empirically the market inefficiency: Evidence from the Paris Stock Exchange. Proceedings of Connectionist Approaches in Economics and Management Sciences (ACSEG 2005), p. 78-89.
Pham, D.-T., Vrins, F., & Verleysen, M. (2005). Spurious entropy minima for multimodal source separation. Proceedings of ISSPA 2005, 37-40.
Lee, J., & Verleysen, M. (2005). Generalisation of the LP norm for time series and its application to Self-Organizing Maps. Proceedings of WSOM 2005, 5th Workshop on Self-Organizing Maps, Paris (France).
Dablemont, S., & Verleysen, M. (2005). Modelling and forecasting of financial time series of “tick data” by functional analysis and neural networks. Proceedings of the Decision Sciences Institute International Conference (DSI′05), p. 159-165.
Simon, G., Lendasse, A., Cottrell, M., Fort, J., & Verleysen, M. (2005). Time series forecasting: Obtaining long term trends with self-organizing maps. Pattern Recognition Letters, 25(12), 1795-1808. https://doi.org/10.1016/j.patrec.2005.03.002 (Original work published 2005)
Yen, L., VanVyve, D. J., Wouters, F., Fouss, F., Verleysen, M., & Saerens, M. (2005). Clustering using a random walk-based distance measure. Proceedings of the 13th European Symposium on Artificial Neural Networks, p. 317-324.
Article de journal
Vrins, F., & Verleysen, M. (2005). On the entropy minimization of a linear mixture of variables for source separation. Signal Processing, 85(5), 1029-1044. https://doi.org/10.1016/j.sigpro.2004.11.026 (Original work published 2005)
Lendasse, A., François, D., Wertz, V., & Verleysen, M. (2005). Vector quantization: a weighted version for time series forecasting. Future Generation Computer Systems, 21(7), 1056-1067. https://doi.org/10.1016/j.future.2004.03.006 (Original work published 2005)
Vrins, F., & Verleysen, M. (2005). Information theoretic versus cumulant-based contrasts for multimodal source separation. IEEE Signal Processing Letters, 12(3), 190-193. https://doi.org/10.1109/LSP.2004.840863 (Original work published 2005)
Lee, J. A., & Verleysen, M. (2005). Nonlinear dimensionality reduction of data manifolds with essential loops. Neurocomputing, 67, 29-53. https://doi.org/10.1016/j.neucom.2004.11.042 (Original work published 2005)
Monographie
Verleysen, M. (2005). 13th European Symposium on Artificial Neural Networks 2005: Proceedings. d-side publ.
Chapitre de livre
Vrins, F., Lee, J., & Verleysen, M. (2005). Filtering-free blind separation of correlated images. In J. Canestany, A. Prieto, F. Sandoval (ed.), Computational Intelligence and Bioinspired Systems, IWANN 2005 (pp. 1091-1099). Springer-verlag Berlin.
Article de journal
Lee, J., Lendasse, A., & Verleysen, M. (2004). Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis. Neurocomputing, 57, 49-76. https://doi.org/10.1016/j.neucom.2004.01.007 (Original work published 2004)
Archambeau, C., Delbeke, J., Veraart, C., & Verleysen, M. (2004). Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind. Artificial Intelligence in Medicine, 32(3), 183-194. https://doi.org/10.1016/j.artmed.2004.02.004 (Original work published 2004)
Simon, G., Lendasse, A., Cottrell, M., Fort, J.-C., & Verleysen, M. (2004). Double quantization of the regressor space for long-term time series prediction: method and proof of stability. Neural Networks, 17(8-9), 1169-1181. https://doi.org/10.1016/j.neunet.2004.08.008 (Original work published 2004)
de Bodt, E., Cottrell, M., Letremy, P., & Verleysen, M. (2004). On the use of self-organizing maps to accelerate vector quantization. Neurocomputing, 56, 187-203. https://doi.org/10.1016/j.neucom.2003.09.009 (Original work published 2004)
Lendasse, A., Cardon, P., Wertz, V., de Bodt, E., & Verleysen, M. (2004). Self-organizing Feature Maps for the Classificaion of Investment Funds. European Journal of Economic and Social Systems, 17(1-2), 183-195. (Original work published 2004)
Benoudjit, N., Cools, E., Meurens, M., & Verleysen, M. (2004). Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models. Chemometrics and Intelligent Laboratory Systems, 70(1-2), 47-53. https://doi.org/10.1016/j.chemolab.2003.10.008 (Original work published 2004)
Dualibe, C., Jespers, P., & Verleysen, M. (2004). Designing Mixed-signal programmable fuzzy logic controllers as embedded subsystems in standard CMOS technologies. Journal of Integrated Circuits and Systems, 1(1), 14-22. (Original work published 2004)
Papier de conférence
Lendasse, A., Oja, E., Simula, O., & Verleysen, M. (2004). Time Series Prediction Competition: The CATS Benchmark. Proceedings of IJCNN′2004 – International Joint Conference on Neural Networks, p. 1615-1620.
Lee, J., Jutten, C., & Verleysen, M. (2004). Non-linear ICA by using isometric dimensionality reduction. Lecture Notes in Computer Science, 3195, 710-717. https://doi.org/10.1007/978-3-540-30110-3_90 (Original work published 2004)
Sorjamaa, A., Lendasse, A., François, D., & Verleysen, M. (2004). Business plans classification with locally pruned lazy learning models. Proceedings of ACSEG 2004, p. 112-119.
François, D., Wertz, V., & Verleysen, M. (2004). Open questions about similarity search in high-dimensional spaces. Proceedings of the 23rd Benelux Meeting on Systems and Control, p. 112.
Archambeau, C., Butz, T., Popovici, V., Verleysen, M., & Thiran, J.-P. (2004). Supervised Nonparametric Information Theoretic Classification. Proceedings of ICPR′04, 17th Intenational Conference on Pattern Recognition, p. 414-417.
François, D., Biga Diambeidou, M., Gailly, B., Wertz, V., & Verleysen, M. (2004). Observer des Trajectoires de ‘Start-up’ sur des Cartes de Kohonen. Proceedings of ACSEG 2004, p. 302-307.
Benoudjit, N., François, D., Meurens, M., & Verleysen, M. (2004). Spectrophotometric variable selection by mutual information. Chemometrics and Intelligent Laboratory Systems, 74(2), 243-251. https://doi.org/10.1016/j.chemolab.2004.04.015 (Original work published 2004)
François, D., Verleysen, M., Wertz, V., Gailly, B., & Biga Diambeidou, M. (2004). Observer des Trajectoires de ‘Start-up’ sur des Cartes de Kohonen. ACSEG 2004, Connectionist Approaches in Economics and Management Sciences, Lille, France.
Delannay, N., Rossi, F., Conan-Guez, B., & Verleysen, M. (2004). Functional Radial Basis Function Network (FRBFN). Proceedings of ESANN 2004, European Symposium on Artificial Neural Networks, p. 313-318.
Donckers, N., Butaye, O., Flandre, D., & Verleysen, M. (2004). Point mémoire analogique basse tension basé sur l’effet GIDL. Proceedings of TAISA 2004 - 5ème Colloque sur le Traitement Analogique de l’Information, du Signal et ses Applications, 55-58.
Lee, J., & Verleysen, M. (2004). How to project ‘circular’ manifolds using geodesic distances? Proceedings of ESANN 2004, European Symposium on Artificial Neural Networks, p. 223-230.
Vrins, F., Bouillon, V., Deswert, J., Bouvy, D., Lee, J., Eugène, C., & Verleysen, M. (2004). On the extraction of the snore acoustic signal by independent component analysis. Proceedings of the Second IASTED International Conference on Biomedical Engineering ( BIOMED), p. 326-331.
Lendasse, A., François, D., Wertz, V., & Verleysen, M. (2004). Sélection de variables spectrales par information mutuelle multivariée pour la construction de modèles non-linéaires. Chimiométrie 2004, Paris (France).
Simon, G., Lee, J., Verleysen, M., & Cottrell, M. (2004). Double Quantization Forecasting Method for Filling Missing Data in the CATS Time Series. Proceedings of IJCNN 2004, International Joint Conference on Neural Networks, p. 1635-1640.
Lendasse, A., Wertz, V., Simon, G., & Verleysen, M. (2004). Fast Bootstrap applied to LS-SVM for long Term Prediction of Time Series. Proceedings of IJCNN 2004, International Joint Conference on Neural Networks, p. 705-710.
Vrins, F., Archambeau, C., & Verleysen, M. (2004). Towards a Local Separation Performances Estimator Using Common ICA Contrast Functions ? Proceedings of ESANN 2004, 211-216.
Archambeau, C., Vrins, F., & Verleysen, M. (2004). Flexible and Robust Bayesian Classification by Finite Mixture Models. Proceedings of ESANN 2004, European Symposium on Artificial Neural Networks, p. 75-80.
Vrins, F., Archambeau, C., & Verleysen, M. (2004). Entropy Minima and Distribution Structural Modifications in Blind Separation of Multimodal Sources. In R. Fisher, R. Preuss, U. von Toussaint (ed.), Proceedings of MaxEnt 2004 (pp. 589-596). American Institute of Physics.
Gailly, B., Wertz, V., Verleysen, M., Biga Diambeidou, M., & François, D. (2004). The Growth Trajectories of Start-Up Firms: An Exploratory Study. ESU entrepreneurship conference, Twente, The Netherlands.
Lendasse, A., Simon, G., Wertz, V., & Verleysen, M. (2004). Fast bootstrap for Least-Square Support Vector Machines. Proceedings of ESANN 2004, European Symposium on Artificial Neural Networks, p. 525-530.
Gailly, B., François, D., Biga Diambeidou, M., Verleysen, M., & Wertz, V. (2004). The Growth Trajectories of Start-Up Firms: An Exploratory Study. Proceedings of the ESU entrepreneurship conference, p. 1-16.
Vrins, F., Vigneron, V., Jutten, C., & Verleysen, M. (2004). Abdominal electrodes analysis by statistical processing for fetal eletrocardiogram extraction. Proceedings of the Second IASTED International Conference on Biomedical Engineering ( BIOMED), p. 244-249.
Dablemont, S., & Verleysen, M. (2004). Classification et prédiction fonctionnelles d’actifs boursiers en intraday. Proceedings of Connectionist Approaches in Economics and Management Sciences (ACSEG 2004), p. 30-38.
Monographie
Verleysen, M. (2004). 12th European Symposium on Artificial Neural Networks. d-side publ.
Chapitre de livre
Vrins, F., Jutten, C., & Verleysen, M. (2004). Sensor array and electrode selection for non-invasive fetal electrocardiogram extraction by independent component analysis. In G. Puntonet, A. Prieto (ed.), Independent Component Analysis and Blind Signal Separation, ICA 2004 (pp. 1017-1024). Springer-Verlag.
Dualibe, C., & Verleysen, M. (2004). Design and application of analog fuzzy logic controllers. In Smart Adaptive Systems on Silicon (p. p. 157-174). Kluwer Academic Publishers.
Papier de conférence
Archambeau, C., Lee, J., & Verleysen, M. (2003). On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures. Proceedings of ESANN 2003, European Symposium on Artificial Neural Networks, p. 99-106.
François, D., Verleysen, M., Wertz, V., Lendasse, A., & Gailly, B. (2003). Should Seed Investors Read Business Plans? 22th Benelux Meeting on Systems and Control, Lommel (Belgium).
Simon, G., Lendasse, A., & Verleysen, M. (2003). Bootstrap for model selection: linear approximation of the optimism. Lecture Notes in Computer Science, 2686, 182-189. https://doi.org/10.1007/3-540-44868-3_24 (Original work published 2003)
Archambeau, C., & Verleysen, M. (2003). Fully nonparametric probability density function estimation with finite Gaussian mixture models. Proceedings of the 5th International Conference on Advances in Pattern Recognition (ICAPR 2003), p. 81-84.
Dablemont, S., Simon, G., Lendasse, A., Ruttiens, A., Blayo, F., & Verleysen, M. (2003). Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction. Proceedings of WSOM 2003, Workshop on Self-Organizing Maps, p. 340-345.
Verleysen, M. (2003). Le test des méthodes neuronales ou comment utiliser les techniques de rééchantillonnage pour ne pas se tromper de résultat. Proceedings of ACSEG 2003, p. 515-534.
Simon, G., Lendasse, A., Cottrell, M., Fort, J.-C., & Verleysen, M. (2003). Double SOM for Long-term Time Series Prediction. Proceedings of WSOM 2003, Workshop on Self-Organizing Maps, p. 340-345.
Verleysen, M., François, D., Simon, G., & Wertz, V. (2003). On the effects of dimensionality on data analysis with neural networks. Lecture Notes in Computer Science, 2687, 105-112. https://doi.org/10.1007/3-540-44869-1_14 (Original work published 2003)
François, D., Lendasse, A., Gailly, B., Wertz, V., & Verleysen, M. (2003). Le plan d’affaires : un outil pour l’investisseur ? Proceedings of ACSEG 2003, p. 239-249.
Lendasse, A., Wertz, V., Simon, G., & Verleysen, M. (2003). Fast Bootstrap for Model Structure Selection. Proceedings of the 22nd Benelux Meeting on Systems and Control, p. 81.
François, D., Verleysen, M., Wertz, V., Gailly, B., & Lendasse, A. (2003). Le plan d’affaires : un outil pour l’investisseur ? ACSEG, 10ème Rencontre Internationale.
Simon, G., Lendasse, A., Wertz, V., & Verleysen, M. (2003). Fast approximation of the bootstrap for model selection. Proceedings of ESANN 2003, European Symposium on Artificial Neural Networks, p. 475-480.
Benoudjit, N., François, D., Meurens, M., & Verleysen, M. (2003). Utilisation de l’information mutuelle pour la sélection de variables spectrales dans des modèles non-linéaires. Proceedings of Chimiométrie 2003, p. 133-136.
Lendasse, A., François, D., Wertz, V., & Verleysen, M. (2003). Nonlinear time series prediction by weighted vector quantization. Lecture Notes in Computer Science, 2657, 417-426. https://doi.org/10.1007/3-540-44860-8_43 (Original work published 2003)
Simon, G., Lendasse, A., Cottrell, M., & Verleysen, M. (2003). Long-term time series forecasting using self-organizing maps : the double vector quantization method. Proceedings of ANNPR 2003, Artificial Neural Networks in Pattern Recognition, p. 8-14.
Lee, J., Archambeau, C., & Verleysen, M. (2003). Locally Linear Embedding versus Isotop. Proceedings of ESANN 2003, European Symposium on Artificial Neural Networks, p. 527-534.
Dablemont, S., Simon, G., Lendasse, A., Ruttiens, A., & Verleysen, M. (2003). Prédiction de séries temporelles financières par double carte de Kohonen et modèles RBFN locaux: application à la prédiction de l’indice boursier DAX30. Proceedings of ACSEG 2003, p. 153-164.
Lendasse, A., Wertz, V., & Verleysen, M. (2003). Model selection with cross-validations and bootstraps - Application to time series prediction with RBFN models. Lecture Notes in Computer Science, 2714, 573-580. (Original work published 2003)
Archambeau, C., Delbeke, J., & Verleysen, M. (2003). Classification of visual sensations generated electrically in the visual field of the blind. Proceedings of IFAC 2003, 5th IFAC Symposium on Modelling and Control in Biomedical Systems, p. 223-228.
Vrins, F., Lee, J. A., Verleysen, M., Vigneron, V., & Jutten, C. (2003). Improving independent component analysis performances by variable selection. 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEECat. No.03TH8718), p. 359-368.
Article de journal
Amerijckx, C., Legat, J.-D., & Verleysen, M. (2003). Image Compression Using Self-Organizing Maps. Systems Analysis Modelling Simulation : journal of mathematical modelling and simulation in systems analysis, 43(11), 1529-1543. https://doi.org/10.1080/0232929032000115182 (Original work published 2003)
Benoudjit, N., & Verleysen, M. (2003). On the kernel widths in radial-basis function networks. Neural Processing Letters, 18(2), 139-154. https://doi.org/10.1023/A:1026289910256 (Original work published 2003)
Chapitre de livre
Lendasse, A., Lee, J., de Bodt, E., Wertz, V., & Verleysen, M. (2003). Approximation by Radial-Basis Function networks - Application to option pricing. In C. Lesage, M. Cottrell eds. (ed.), Connectionist Approaches in Economics and Management Sciences (p. p. 203-214). Kluwer academic publishers.
Verleysen, M. (2003). Learning high-dimensional data. In S. Ablameyko, L. Goras, M. Gori, V. Piuri (ed.), Limitations and Future Trends in Neural Computation (p. p. 141-162). IOS Press.
Monographie
Dualibe, C., Jespers, P., & Verleysen, M. (2003). Design of Analog Fuzzy Logic Controllers in CMOS Technologies: Implementation, Test and Applications. Kluwer academic publishers.
Verleysen, M. (2003). 11th European Symposium on Artificial Neural Networks 2003: Proceedings. d-side publ.
Article de journal
de Bodt, E., Cottrell, M., & Verleysen, M. (2002). Statistical tools to assess the reliability of self-organizing maps. Neural Networks, 15(8-9), 967-978. https://doi.org/10.1016/S0893-6080(02)00071-0 (Original work published 2002)
De Lima, J. A., Silva, S. F., Cordeiro, A. S., & Verleysen, M. (2002). A CMOS/SOI single-input PWM discriminator for low-voltage body-implanted applications. VLSI Design, 15(1), 469-476. https://doi.org/10.1080/1065514021000012075 (Original work published 2002)
Verleysen, M., & Vandewalle, J. (2002). Fundamental and information processing aspects of neurocomputing. Neurocomputing, 136(special). (Original work published 2014)
Verleysen, M., & Vandewalle, J. (2002). Special issue on fundamental and information processing aspects of neurocomputing. Neurocomputing, 48(1-4), 1-2. https://doi.org/10.1016/S0925-2312(01)00667-1 (Original work published 2002)
Lee, J., & Verleysen, M. (2002). Self-organizing maps with recursive neighborhood adaptation. Neural Networks, 15(8-9), 993-1003. (Original work published 2002)
Papier de conférence
Havran, C., Hupet, L., Czyz, J., Lee, J., Vandendorpe, L., & Verleysen, M. (2002). Independent component analysis for face authentication. In Damiani, E.; Howlett, R.J.; Jain, L.C.; Ichalkaranje, N. (ed.), Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies (pp. 1207-1211). IOS press.
Lendasse, A., Cottrell, M., Wertz, V., & Verleysen, M. (2002). Prediction of Electric Load using Kohonen Maps - Application to the Polish Electricity Consumption. Proceedings of the American Control Conference 2002 (ACC 2002), p. 3684-3689.
Benoudjit, N., Cools, E., Meurens, M., & Verleysen, M. (2002). Calibrage chimiométrique des spectrophotomètres : sélection et validation des variables par modèles non-linéaires. Proceedings of Chimiométrie 2002, p. 25-28.
Cardon, P., Lendasse, A., Wertz, V., de Bodt, E., & Verleysen, M. (2002). Classification of investment funds by self-organizing maps. Proceedings of ACSEG 2002, p. 201-212.
Lendasse, A., Lee, J., Wertz, V., & Verleysen, M. (2002). Forecasting electricity consumption using nonlinear projection and self-organizing maps. Neurocomputing, 48, 299-311. (Original work published 2002)
Lee, J., Lendasse, A., & Verleysen, M. (2002). Curvilinear Distance Analysis versus Isomap. Proceedings of ESANN 2002, European Symposium on Artificial Neural Networks, p. 185-192.
Kervyn, T., Donckers, N., Verleysen, M., & Flandre, D. (2002). Multiplieur analogique en technologie SOI pour le décodage de turbo-codes. Proceedings du Colloque TAISA 2002, 69-73.
Lendasse, A., Cottrell, M., Wertz, V., & Verleysen, M. (2002). Prediction of electric load using Kohonen maps - Application to the Polish electricity consumption. Proceedings of the 2002 American Control Conference (IEEE Cat.No.CH37301), Vol. 5, p. 3684-9. https://doi.org/10.1109/ACC.2002.1024500
Lee, J., & Verleysen, M. (2002). Nonlinear projection with the isotop method. Lecture Notes in Computer Science, 2415, 933-938. https://doi.org/10.1007/3-540-46084-5_151 (Original work published 2002)
Benoudjit, N., Archambeau, C., Lendasse, A., Lee, J., & Verleysen, M. (2002). Width optimization of the Gaussian kernels in Radial Basis Function Networks. In Verleysen, M. (ed.), 10th European Symposium on Artificial Neural Networks. ESANN′2002.Proceedings (p. p. 425-432). D-side publications.
Hubaux, D., Guedria, L., Vandendorpe, L., Verleysen, M., & Legat, J.-D. (2002). Nouvelles méthodes de conception de systèmes électroniques intégrés. Proceedings of SympA′8, p. 341-345.
Chapitre de livre
Verleysen, M. (2002). The explanatory power of Artificial Neural Networks. In R. Franck ed. (ed.), The explanatory power of models (p. p. 127-139). Kluwer academic publishers.
Monographie
Verleysen, M. (2002). 10th European Symposium on Artificial Neural Networks 2002: Proceedings. d-side publ.
Papier de conférence
de Bodt, E., Cottrell, M., & Verleysen, M. (2001). Are they Really Neighbor ? A Statistical Analysis of the SOM Algorithm Output. AISTATS′2001 proceedings, p. 35-40.
Lee, J., Donckers, N., & Verleysen, M. (2001). Recursive learning rules for SOMs. In N. Allinson, H. Yin, L. Allinson, J. Slack (ed.), Advances in Self-Organizing Maps (p. p. 67-72). Springer Verlag.
Cottrell, M., de Bodt, E., & Verleysen, M. (2001). A Statistical Tool to Assess the Reliability of Self-Organizing Maps. In N. Allinson, H. Yin, L. Allinson, J. Slack (ed.), Advances in Self-Organizing Maps (p. p. 7-14). Springer Verlag.
Verleysen, M. (2001). Learning high-dimensional data. Proceedings of LFTNC 2001, p. 22.
Dualibe, C., Jespers, P., & Verleysen, M. (2001). Embedded fuzzy control for automatic channel equalization after digital transmissions. Proceedings of ISCAS 2001, International Symposium on Circuits and Systems, p. 173-176.
Lendasse, A., Wertz, V., & Verleysen, M. (2001). Forecasting electricity demand using Kohonen maps. Proeedings of the 20th Benelux meeting on Systems and Control, p. 118.
Dualibe, C., Jespers, P., & Verleysen, M. (2001). On Designing Mixed-Signal Programmable Fuzzy Logic Controllers as Embedded Subsystems in Standard CMOS Technologies. Proceedings of SBCCI′2001, 14th Symposium on Integrated Circuits and System Design, p. 194-200.
Archambeau, C., Lendasse, A., Trullemans, C., Veraart, C., Delbeke, J., & Verleysen, M. (2001). Phosphene evaluation in a visual prosthesis with artificial neural networks. Proceedings of EUNITE 2001, European Symposium on Intelligent Techniques, Hybrid Systems and their implementation on Smart Adaptive Systems, p. 509-515.
Lendasse, A., Lee, J., de Bodt, E., Wertz, V., & Verleysen, M. (2001). Input data reduction for the prediction of financial time series. In Verleysen, M.; (ed.), 9th European Symposium on Artificial Neural Networks. ESANN′2001.Proceedings (p. p. 237-244). D-facto.
Lendasse, A., Lee, J., de Bodt, E., Wertz, V., & Verleysen, M. (2001). Approximation using Radial Basis Function Networks - Application to Pricing Derivative Securities. Proceedings of ACSEG 2001, p. 275-283.
Article de journal
Lendasse, A., Lee, J., de Bodt, E., Wertz, V., & Verleysen, M. (2001). Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index. European Journal of Economic and Social Systems, 15(2), 31-48. https://doi.org/10.1051/ejess:2001114 (Original work published 2001)
Monographie
Verleysen, M. (2001). 9th European Symposium on Artificial Neural Networks 2001: Proceedings. D facto publ.
Monographie
Verleysen, M. (2000). 8th European Symposium on Artificial Neural Networks 2000: Proceedings. D facto publ.
Papier de conférence
Lendasse, A., Lee, J., Wertz, V., & Verleysen, M. (2000). Time Series Forecasting using CCA and Kohonen Maps - Applications to Electricity Consumption. Proceedings of ESANN 2000, European Symposium on Artificial Neural Networks, p. 329-334.
Lee, J., Lendasse, A., Donckers, N., & Verleysen, M. (2000). A robust nonlinear projection method. Proceedings of ESANN 2000, European Symposium on Artificial Neural Networks, p. 13-20.
Cavalcanti, C., de Lima, J. A., & Verleysen, M. (2000). A CMOS/SOI Continuous-Time Low-Pass gm-C Filter. Proceedings of the XV SBMicro International Conference on Microelectronics & Packaging. XV SBMicro International Conference on Microelectronics & Packaging, Manaus (Brazil).
Dualibe, C., Jespers, P., & Verleysen, M. (2000). A 5.26 Mflips Programmable Analogue Fuzzy Logic Controller in a Standard CMOS 2.4 microns Technology. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS′2000), p. 377-380.
Donckers, N., Dualibe, C., & Verleysen, M. (2000). A current-mode CMOS loser-take-all with minimum function for neural computations. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS′2000), p. 415-418.
De Lima, J., Silva, S., Cordeiro, A., Araujo, A., & Verleysen, M. (2000). A low-power silicon-on-insulator PWM discriminator for biomedical applications. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS′2000), p. 277-280.
Doguet, P., Mevel, H., Verleysen, M., Troosters, M., & Trullemans, C. (2000). An Integrated Circuit for the Electrical Stimulation of the Optic Nerve. Proceedings of the 5th annual conference of the International Functional Electrical Stimulation Society (IFESS′2000), p. 309-312.
Lendasse, A., Lee, J., de Bodt, E., Wertz, V., & Verleysen, M. (2000). Réduction de la dimension d’un ensemble d’indicateurs techniques en vue de la prédiction de séries temporelles financières - Application à l’indice de marché BEL 20. ACSEG 2000 proceedings - Connectionist Approaches in Economics and Management Sciences, p. 155-175.
Article de journal
Lendasse, A., de Bodt, E., Wertz, V., & Verleysen, M. (2000). Non linear financial time series forecasting - Aplication to the Bel 20 stock market index. European Journal of Economic and Social Systems, 14(1), 81-91. https://doi.org/10.1051/ejess:2000110 (Original work published 2000)
Papier de conférence
Vanlierde, A., Trullemans, C., Veraart, C., Michaux, G., Mortimer, J. T., Delbeke, J., Wanet-Defalque, M.-C., Verleysen, M., Glineur, O., & Parrini, S. (1999). Selective stimulation of the human optic nerve. Proceedings of the 4th Conference of the International Functional Electrical Stimulation Society, p. 27-59.
de Bodt, E., Cottrell, M., & Verleysen, M. (1999). Using the Kohonen Algorithm for Quick Initialization of Simple Competitive Learning Algorithm. Proceedings of the European Symposium on Artificial Neural Networks (ESANN′99), p. 19-26.
Lendasse, A., Verleysen, M., Donckers, N., & Wertz, V. (1999). Extraction of intrinsic dimension using CCA-Application to blind sources separation. Proceedings of the European Symposium on Artificial Neural Networks (ESANN′99), p. 339-344.
Lendasse, A., de Bodt, E., & Verleysen, M. (1999). Forecasting financial time series through intrinsic dimension estimation and non-linear data projection. In J. Mira, J. Sanchez-Andres eds. (ed.), Engineering Applications of Bio-Inspired Artificial Neural Networks (pp. II596-II605). Springer. https://doi.org/10.1007/BFb0100527
Dualibe, C., Verleysen, M., & Donckers, N. (1999). Design of Complementary Low-Power CMOS Architectures for Looser-take-all and Winner-take-all. Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems (MicroNeuro′99), p. 360-365.
Monographie
Verleysen, M. (1999). 7th European Symposium on Artificial Neural Networks 1999: Proceedings. D facto publ.
Chapitre de livre
Verleysen, M., LENDASSE, A., & de Bodt, E. (1999). Forecasting financial time series through intrinsic dimension estimation and non-linear data projection.
Papier de conférence
LENDASSE, A., Grégoire, P., Verleysen, M., Cottrell, M., & de Bodt, E. (1998). Forecasting Time-Series by Kohonen Classification. Proceedings of ESANN′98, Bruges.
Trullemans, C., Amerijckx, C., Mevel, H., Legat, J.-D., Verleysen, M., Troosters, M., & Doguet, P. (1998). An electronic device for nerve stimulation. Workshop on Industrial Microtechnology Applications, Madrid (Spain).
Lendasse, A., de Bodt, E., & Verleysen, M. (1998). Estimation de la dimension intrinsèque d’une série temporelle et prédiction par une méthode de projection. Application au SBF 250 sur la période 1992-1997. Proceedings of ACSEG 1998, Connectionist Approaches in Economics and Management Sciences, p. D-37 - D-46.
Verleysen, M. (1998). The explanatory power of Artificial Neural Networks. Proceedings of Methodos: The explanatory power of models in the social sciences, p. 13 pages.
Lendasse, A., de Bodt, E., Cottrell, M., Verleysen, M., & Grégoire, P. (1998). Forecasting Time-Series by Kohonen Classification. Proceedigns of ESANN′98, European Symposium on Artificial Neural Networks, p. 221-226.
Article de journal
Amerijckx, C., Legat, J.-D., Verleysen, M., & Thissen, P. (1998). Image compression by self-organized Kohonen map. IEEE Transactions on Neural Networks, 9(3), 503-507. https://doi.org/10.1109/72.668891 (Original work published 1998)
Dualibe, C., Jespers, P., & Verleysen, M. (1998). Two-quadrant CMOS analogue divider. Electronics Letters, 34(12), 1164-1165. https://doi.org/10.1049/el:19980861 (Original work published 1998)
Monographie
Verleysen, M. (1998). 6th European Symposium on Artificial Neural Networks 1998: Proceedings. D facto publ.
Papier de conférence
Parramon, J., Doguet, P., Marin, D., Verleysen, M., Munoz, R., Leija, L., & Valderrama Opazo, E. (1997). Asic-based batteryless implantable telemetry microsystem for recording purposes. Proceedings of the 19th IEEE-EMBS Engineering in Medecine and Biology Society Conference, p. 2225-2228.
Parramon, J., Doguet, P., Verleysen, M., & Valderrama, E. (1997). Design of a RF powered mixed-mode implantable IC for a wireless EMG recording purpose. Proceedings of Iberchip′97, p. 460-468.
Parramon, J., Doguet, P., Marin, D., Verleysen, M., Valderrama, E., Arzuaga, J., & Leija, L. (1997). IC based RF powered implantable telemetry microsystem for EMG recording. In T.Penzel, S.Salmons, M.Neuman eds. (ed.), Proceedings of the XIV International Symposium on Biotelemetry (p. p. 257-263).
Article de journal
Hlavackova, K., & Verleysen, M. (1997). Placing spline knots in neural networks using splines as activation functions. Neurocomputing, 17(3-4), 159-167. https://doi.org/10.1016/S0925-2312(97)00053-2 (Original work published 1997)
Monographie
Verleysen, M. (1997). 5th European Symposium on Artificial Neural Networks 1997: Proceedings. D facto publ.
Monographie
Verleysen, M. (1996). 4th European Symposium on Artificial Neural Networks 1996: Proceedings. D facto publ.
Blayo, F., & Verleysen, M. (1996). Les Réseaux de Neurones Artificiels. Presses Universitaires de France.
Chapitre de livre
Verleysen, M. (1996). Feedforward unsupervised models. In E.Fiesler, R.Bealer eds. (ed.), Handbook on Neural Computation (p. p. C2.1:1-C2.1:15). IOP and Oxford University Press.
Papier de conférence
Verleysen, M., & Hlavackova, K. (1996). Learning in RBF networks. Proceedings of the International Conference on Neural Networks (ICNN′96), p. 199-204.
Thissen, P., Verleysen, M., & Legat, J.-D. (1996). A VLSI Associative Processor for Neural-Like Classification Algorithms. Proc. of MCPS′96, p. 130-136.
Article de journal
Madrenas, J., Verleysen, M., Thissen, P., & Voz, JL. (1996). A CMOS analog circuit for Gaussian functions. IEEE Transactions on Circuits and Systems Part 2: Analog and Digital Signal Processing, 43(1), 70-74. https://doi.org/10.1109/82.481479 (Original work published 1996)
Papier de conférence
Verleysen, M., Voz, J.-L., Thissen, P., & Legat, J.-D. (1995). A statistical neural network for high-dimensional vector classification. Proc. of the International Conference on Neural Networks, p. 990-994.
Verleysen, M. (1995). Les principaux modèles de réseaux de neurones artificiels. In E. de Bodt, E.Henrion eds. (ed.), Proceedings de “Les réseaux de neurones en finance : conception et applications” (p. p. 59-97). D-Facto publications.
Thissen, P., Verleysen, M., & Legat, J.-D. (1995). A FPGA-based control unit for dedicated parallel processor. Proc. of the ProRISC/IEEE Workshop on Circuit, Systems and Signal Processing, p. 303-308.
Thissen, P., Legat, J.-D., Verleysen, M., Madrenas, J., & Dominguez, J. (1995). A VLSI system for neural Bayesian and LVQ classification. Lecture Notes in Computer Science, 930, 696-703. (Original work published 1995)
Thissen, P., Verleysen, M., & Legat, J.-D. (1995). An associative processor architecture for pattern recognition. Proc. of the ProRISC/IEEE Workshop on Circuit, Systems and Signal Processing, p. 309-315.
Voz, JL., Legat, J.-D., Verleysen, M., & Thissen, P. (1995). A practical view of suboptimal Bayesian classification with radial Gaussian kernels. Lecture Notes in Computer Science, 930, 404-411. (Original work published 1995)
Voz, J.-L., Verleysen, M., Thissen, P., & Legat, J.-D. (1995). Suboptimal Bayesian classification by vector quantization with small clusters. Proc. of the European Symposium on Artificial Neural Networks, p. 153-160.
VOZ, J.-L., & Verleysen, M. (1995). Problématique générale du design des classifieurs et protocoles expérimentaux utilisés dans le cadre d’un projet ESPRIT relatif à l’application des réseaux de neurones à la classification. Proceedings des 3èmes rencontres de la société francophone de classification, p. 60-61.
Monographie
Verleysen, M. (1995). 3rd European Symposium on Artificial Neural Networks 1995: Proceedings. D facto publ.
Papier de conférence
Verleysen, M., THISSEN, P., & Madrenas, J. (1994). Analog VLSI implementation of kernel-based classifiers. Belgian Neural Network Contact Group, p. 138-144.
Voz, J.-L., Verleysen, M., Thissen, P., & Legat, J.-D. (1994). Handwritten digit recognition by suboptimal bayesian classifier. Proc of NeuroNîmes94, p. 17-26.
Verleysen, M., & Hlavackova, K. (1994). An optimized RBF network for approximation of functions. Proceedings of ESANN 2004, European Symposium on Artificial Neural Networks, p. 175-180.
Legat, J.-D., Thissen, P., Verleysen, M., & Voz, J.-L. (1994). Parallel implementations of the RCE algorithm. Proc. of the Cost 229 final workshop on adaptive systems, intelligent approaches, massively computing and emerging techniques in signal processing and telecommunications, p. 261-264.
Comon, P., VOZ, J.-L., & Verleysen, M. (1994). Estimation of Performance Bounds in Supervised Classification. Proceedings of ESANN′94, European Symposium on Artificial Neural Networks, p. 37-42.
Voz, J.-L., Thissen, P., Verleysen, M., & Legat, J.-D. (1994). Application of suboptimal Bayesian classification to handwritten numerals recognition. Proc. of the IEE European workshop on handwriting analysis and recognition : an European perspective, p. 97-106.
Thissen, P., Verleysen, M., & Legat, J.-D. (1994). Matching properties of CMOS SOI transistors. Proc. of the 4th int. conf. on microelectronics for neural networks and fuzzy systems, p. 134-137.
Monographie
Verleysen, M., & de Bodt, E. (1994). Belgian Neural Network Contact Group: 1994 annual meeting.
Verleysen, M. (1994). 2nd European Symposium on Artificial Neural Networks 1994: Proceedings. D facto publ.
Article de journal
Verleysen, M., & Cabestany, J. (1994). Projet ESPRIT ELENA. Réalisations VLSI de réseaux de neurones. Revue VALGO, bulletin de l’ACTH n°94-1, 94(1), 75-87. (Original work published 1994)
Verleysen, M., Thissen, P., Voz, JL., & Madrenas, J. (1994). An Analog Processor Architecture for a Neural-network Classifier. IEEE Micro, 14(3), 16-28. https://doi.org/10.1109/40.285221 (Original work published 1994)
Article de journal
Macq, D., Jespers, P., Legat, J.-D., & Verleysen, M. (1993). Analog Implementation of a Kohonen Map With On-chip Learning. IEEE Transactions on Neural Networks, 4(3), 456-461. https://doi.org/10.1109/72.217188 (Original work published 1993)
Chapitre de livre
Verleysen, M., Legat, J.-D., & Jespers, P. (1993). Analog Implementation of an Associative Memory : Learning Algorithm and VLSI Constraints. In M. Hassoun ed. (ed.), Associative Neural Memories: Theory and Implementation. Oxford University Press.
Papier de conférence
Verleysen, M., Legat, J.-D., & Thissen, P. (1993). Optimal decision surfaces in LVQ1 classification of patterns. In Verleysen, M.; (ed.), European Symposium on Artificial Neural Networks ESANN ’93. Proceedings (p. p. 209-214). D facto.
Simon, B., Macq, B., & Verleysen, M. (1993). Laplacian Pyramid with Multilayer Perceptrons Interpolators. Proceedings of the European Symposium on Artificial Neural Networks (ESANN′93), p. 137-144.
Verleysen, M., Thissen, P., & Legat, J.-D. (1993). Learning vector classification: an improvement on LVQ algorithms to create classes of patterns. In J.Mira, J.Cabestany, A.Prieto eds. (ed.), Proceedings of the International Workshop on Artificial Neural Networks (IWANN 1993) (p. p. 340-345). Springer-Verlag.
Verleysen, M. (1993). NERVES and ELENA : the Basic Research on Artificial Neural Networks in Europe. Proceedings of the European Informatic Congress - Computing Systems Architectures, p. 157-167.
Simon, B., Macq, B., & Verleysen, M. (1993). Pyramids for image compression with neural networks interpolators. Proceedings of the 14th Symposium for Information Theory in the Benelux, p. 168-174.
Verleysen, M. (1993). Realizations of artificial neural networks : VLSI, machines and neural computers. Proceedings of Neuro-Nîmes′93, p. 120 pages.
Blayo, F., & Verleysen, M. (1993). Neural networks models. Proceedings of Neuro-Nîmes′93, p. 110 pages.
Monographie
Verleysen, M. (1993). 1st European Symposium on Artificial Neural Networks 1993: Proceedings. D facto publ.
Papier de conférence
Verleysen, M., & Legat, J.-D. (1992). Une procédure d’initialisation de type LVQ pour le réseau RCE. Proc. du Congrès européen de mathématiques, p. 35-45.
Blayo, F., & Verleysen, M. (1992). Setting Initial Conditions for the RCE Model. Proceedings of the 1st IFIP Working Group 10.6 Workshop, p. 31-35.
Verleysen, M., Blayo, F., & Legat, J.-D. (1992). LVQ-like procedure for the initialization of the RCE model. Congrès Satellite du Congrès Européen de Mathématiques: Aspects Théoriques des Réseaux de Neurones, Paris (France).
Legat, J.-D., Cornil, J.-P., & Verleysen, M. (1992). Parallel VLSI-based Architecture for Multi-motion Estimation. Proc. of the Int. SPIE Conf. on Applications of Artificial Intelligence X : Machine Vision and Robotics, p. 165-171.
Verleysen, M. (1992). Realizations of artificial neural networks : VLSI, machines and neural computers. Proceedings of Neuro-Nîmes′92, p. 114 pages.
Hanssens, E., Verleysen, M., & Legat, J.-D. (1992). A dedicated neural network for visual motion detection. Proc. of Neuro-Nîmes′92, p. 221-229.
Legat, J.-D., Cornil, J. P., Macq, D., & Verleysen, M. (1992). A real-time VLSI-based architecture for multi-motion estimation. Proceedings. 11th IAPR International Conference on Pattern Recognition.Vol. IV. Conference D: Architectures for Vision and Pattern Recognition, p. 147-150. https://doi.org/10.1109/ICPR.1992.202152
Thèse
Verleysen, M. (1992). Neural networks and content-addressable memories for vision: from theory to VLSI.
Papier de conférence
Verleysen, M., & Jespers, P. (1991). Stochastic computations in VLSI analog neural networks. Proceedings of the International Conference on Artificial Neural Networks, p. 1691-1696.
Verleysen, M., & Jespers, P. (1991). Analog VLSI synapse matrix with enhanced stochastic computations. In Prieto, Alberto (ed.) (ed.), Proceedings of the International Workshop on Artificial Neural networks (pp. 315-321). Springer-Verlag. https://doi.org/10.1007/BFb0035908
Verleysen, M., & Jespers, P. (1991). Stochastic computations in VLSI analog neural networks. Proceedings of Neural Networks in Natural and Artificial Vision, p. 53-56.
Legat, J.-D., Cornil, J.-P., & Verleysen, M. (1991). Systolic array architecture for Early vision processing. Proc. of ESSCIRC′91, p. 93-96.
Chapitre de livre
Verleysen, M., & Jespers, P. (1991). Precision of Computations in Analog Neural Networks. In U.Ramacher et U.Rückert eds. (ed.), VLSI design of neural networks (p. p. 65-82). Kluwer Academic Publishers (UK).
Verleysen, M., & Jespers, P. (1991). VLSI Chips for Neural Networks. In Omid M. Omidvar ed. (ed.), Progress in Neural Networks (p. p. 175-196). Ablex Publishing Corporation.
Papier de conférence
Verleysen, M., & Jespers, P. (1990). Precision of sum-of-products in analog neural networks. Proceedings of the 1st International Workshop on Microelectronics for neural Networks, p. 47-61.
Article de journal
Verleysen, M., Sirletti, B., Vandemeulebroecke, A., & Jespers, PGA. (1989). A High-storage Capacity Content-addressable Memory and its Learning Algorithm. IEEE Transactions on Circuits and Systems, 36(5), 762-766. https://doi.org/10.1109/31.31325 (Original work published 1989)
Verleysen, M., Sirletti, B., Vandemeulebroecke, A., & Jespers, P. (1989). Neural networks for high-storage content-addressable memories: VLSI circuit and learning algorithm. IEEE Journal of Solid State Circuits, 24(3), 562-569. https://doi.org/10.1109/4.32008 (Original work published 1989)
Verleysen, M., & Jespers, PGA. (1989). An Analog Vlsi Implementation of Hopfield’s Neural Network. IEEE Micro, 9(6), 46-55. https://doi.org/10.1109/40.42986 (Original work published 1989)
Papier de conférence
Verleysen, M., Martin, D., & Jespers, P. (1989). A VLSI neural network with capacitive synapses. Proceedings of the European Conference on Circuit Theory and Design, p. 73-77.
Verleysen, M., Jespers, P., & Martin, D. (1989). A capacitive neural network for associative memory. In Barbe, A.M.; (ed.), Proceedings of the Tenth Symposium on Information Theory in the Benelux (p. p. 73-79). Werkgemeenschap voor inf.- & communicatietheorie.
Verleysen, M., & Jespers, P. (1989). Implémentations VLSI analogiques de réseaux de neurones. Journées d’électronique, p. 279-289.
Verleysen, M., Sirletti, B., & Jespers, P. (1989). A new VLSI architecture for neural associative memories. Neural Networks from Models to Applications, p. 692-700.
Verleysen, M., & Jespers, P. (1989). An analog VLSI architecture for large neural networks. In F.Fogelman-Soulié & J.Hérault eds. (ed.), Neurocomputing (p. p. 141-145). Springer-Verlag.
Papier de conférence
Verleysen, M., Sirletti, B., & Jespers, P. (1988). A large VLSI Hopfield network for pattern recognition problems. Proceedings of the 1988 International Neural Network Society Annual Meeting, p. 414.
Sirletti, B., Verleysen, M., & Jespers, P. (1988). An algorithm for pattern recognition with VLSI neural networks. 1988 International Neural Network Society Annual Meeting, Boston (USA).
Verleysen, M., Sirletti, B., & Jespers, P. (1988). A large VLSI fully interconnected neural network. 1988 Symposium on VLSI circuits, Tokyo (Japan).
Verleysen, M., Sirletti, B., & Jespers, P. (1988). A new VLSI architecture for large Hopfield’s neural networks. Proceedings of the 14th European Solid-State Circuits Conference (ESSCIRC 1988), p. 343-346.
Verleysen, M., Sirletti, B., & Jespers, P. (1988). A new CMOS architecture for neural networks. VLSI for Artificial Intelligence, p. 209-217.
Papier de conférence
Lendasse, A., François, D., Wertz, V., & Verleysen, M. (n.d.). Vector quantization: a weighted version for time-series forecasting. 5th International Conference on Computational Sciences (ICCS 2005), Atlanta (USA).
