Publications sur la spectrométrie et la chimiométrie

• Articles
• Conférences internationales avec comité de lecture et publication des actes
• Conférences nationales avec comité de lecture et publication des actes

Articles

• (7) A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis. Catherine Krier, Fabrice Rossi, Damien François et Michel Verleysen. Chemometrics and Intelligent Laboratory Systems, volume 91, numéro 1, pages 43-53. March 2008.
  [lien éditeur] [hal] [arXiv:0802.0287] [preprint (pdf)] [bib]
• (6) Fast Selection of Spectral Variables with B-Spline Compression. Fabrice Rossi, Damien François, Vincent Wertz, Marc Meurens et Michel Verleysen. Chemometrics and Intelligent Laboratory Systems, volume 86, numéro 2, pages 208-218. April 2007.
  [lien éditeur] [hal] [arXiv:0709.3639v1] [preprint (ps)] [preprint (pdf)] [bib]
  Cité par +/-

  1. L. Komsta, R. Skibiński, M. Paryło and K. Dudek. The Basis Function Regression in pharmaceutical analysis Theory and example of application. Journal of Pharmaceutical and Biomedical Analysis 47(4-5):659-669, August 2008.
• (5) Support Vector Machine For Functional Data Classification. Fabrice Rossi et Nathalie Villa. Neurocomputing, volume 69, numéro 7-9, pages 730-742. March 2006.
  [lien éditeur] [hal] [arXiv:0705.0209v1] [preprint (ps)] [preprint (pdf)] [bib]
  Cité par +/-

  1. B. Li and Q. Yu Classification of functional data: A segmentation approach. Computational Statistics & Data Analysis, 52(10):4790-4800, June 2008.
  2. C. Park, J.-Y. Koo, S. Kim, I. Sohn, and J. W. Lee. Classification of gene functions using support vector machine for time-course gene expression data. Computational Statistics & Data Analysis, 52(5):2578-2587, January 2008.
  3. Z.-J. Lee. An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer. Artificial Intelligence in Medicine, 42(1):81-93, January 2008.
  4. T. Laloë. A k-nearest neighbor approach for functional regression. Statistics & Probability Letters, 78(10):1189-1193, August 2008.
  5. Z.-J. Lee, S.-W. Lin, C.-C. V. Hsu, and Y.-P. Huang. Gene extraction and identification tumor/cancer for microarray data of ovarian cancer. In Proceedings of TENCON 2006 (IEEE Region 10 Conference), Hong-Kong, November 2006.
• (4) Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Fabrice Rossi, Amaury Lendasse, Damien François, Vincent Wertz et Michel Verleysen. Chemometrics and Intelligent Laboratory Systems, volume 80, numéro 2, pages 215-226. February 2006.
  [lien éditeur] [hal] [arXiv:0709.3427v1] [preprint (ps)] [preprint (pdf)] [bib]
  Cité par +/-

  1. A.H. Vahabie, M.M.R. Yousefi, B.N. Araabi, C. Lucas, S. Barghinia, and P.Ansarimehr. Mutual Information Based Input Selection in Neuro-Fuzzy Modeling for Short Term Load Forecasting of Iran National Power System. In Proceedings of the IEEE International Conference on Control and Automation (ICCA 2007), pages 2710-2715 , May 30 2007-June 1 2007.
  2. K. Hlavackova-Schindler, M. Palus, M. Vejmelka, and J. Bhattacharya. Causality detection based on information-theoretic approaches in time series analysis, Physics Reports, 441(1):1-46, 2007.
  3. L. Herrera, H. Pomares, I. Rojas, A. Guillen, A. Prieto, and O. Valenzuela. Recursive prediction for long term time series forecasting using advanced models. Neurocomputing, 70(16-18):2870-2880, October 2007.
  4. C. Gomez, R. Hornero, D. Abasolo, A. Fernandez, and J. Escudero. Analysis of the magnetoencephalogram background activity in alzheimer's disease patients with auto-mutual information. Computer Methods and Programs in Biomedicine, 87(3):239-247, 2007.
  5. A. Durand, O. Devos, C. Ruckebusch, and J. Huvenne. Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton-viscose textiles. Analytica Chimica Acta, 595(1-2):72-79, 2007.
  6. M. M. R. Yousefi, M. Mirmomeni, and C. Lucas. Input variables selection using mutual information for neuro fuzzy modeling with the application to time series forecasting. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2007), pages 1121-1126, Orlando, Florida (U.S.A.), August 2007.
  7. F. Corona, S. Sassu, Lorenzo and. Melis, and R. Baratti. Measure of topological relevance for soft sensing product properties. In Proceedings of the 8th IFAC International Symposium on Dynamics and Control of Process Systems (DYCOPS 2007), pages 175-180, June 2007.
  8. C. Tan, M. Li, and X. Qin. Random Subspace Regression Ensemble for Near-Infrared Spectroscopic Calibration of Tobacco Samples. Analytical Sciences, 24(5):647-653, May 2008.
  9. Q. Noirhomme, R.I. Kitney, and B. Macq. Single-Trial EEG Source Reconstruction for Brain–Computer Interface. IEEE Transactions on Biomedical Engineering, 55(5):1592-1601, May 2008.
• (3) Un modèle neuronal pour la régression et la discrimination sur données fonctionnelles. Fabrice Rossi et Brieuc Conan-Guez. Revue de Statistique Appliquée, volume LIII, numéro 4, pages 5-30. October 2005.
  [hal] [preprint (ps)] [preprint (pdf)] [bib]
• (2) Representation of Functional Data in Neural Networks. Fabrice Rossi, Nicolas Delannay, Brieuc Conan-Guez et Michel Verleysen. Neurocomputing, volume 64, pages 183-210. March 2005.
  [lien éditeur] [hal] [arXiv:0709.3641v1] [preprint (ps)] [preprint (pdf)] [bib]
  Cité par +/-

  1. F. Corona and A. Lendasse. Input selection and function approximation using the SOM: an application to spectrometric modeling. In Proceedings of the 5th Workshop on Self-Organizing Maps (WSOM 05), pages 653-660, Paris (France), September 2005.
  2. A. Vellido, P. J.G. Lisboa and D. Vicente, Robust analysis of MRS brain tumour data using t-GTM, Neurocomputing, 69(7-9):754-768, March 2006.
  3. M. Strickert, U. Seiffert, N. Sreenivasulu, W. Weschke, T. Villmann and B. Hammer, Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis, Neurocomputing, 69(7-9):651-659, March 2006.
  4. B.-J. Park, W. Pedrycz, and S.-K. Oh, Fuzzy polynomial neurons as neurofuzzy processing units, Neural Computing & Applications, 15(3-4):310-327, June 2006.
  5. K.-L. Du and M.N.S. Swamy. Neural Networks in a Softcomputing Framework. Springer, 2006.
  6. T. Villmann, E. Merényi, and U. Seiffert. Machine Learning Approaches and Pattern Recognition for Spectral Data, in Proceedings of XVIth European Symposium on Artificial Neural Networks (ESANN 2008), pages 433-444, Bruges (Belgium), April 2008.
• (1) Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis. Fabrice Rossi et Brieuc Conan-Guez. Neural Networks, volume 18, numéro 1, pages 45-60. January 2005.
  [lien éditeur] [hal] [arXiv:0709.3642v1] [preprint (ps)] [preprint (pdf)] [bib]
  Cité par +/-

  1. Vellido, A. and Lisboa, P.J.G. Functional topographic mapping for robust handling of outliers in brain tumour data. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2005), pages 133-138, Bruges (Belgium), April 2005.
  2. B.-J. Park, W. Pedrycz, and S.-K. Oh, Fuzzy polynomial neurons as neurofuzzy processing units, Neural Computing & Applications, 15(3-4):310-327, June 2006.
  3. L. Ferré and N. Villa. Multilayer perceptron with functional inputs: an inverse regression approach. Scandinavian Journal of Statistics, 33 (4), 807-823, December 2006.
  4. J.M. Poggi and C. Tuleau, Classification supervisée en grande dimension. Application à l'agrément de conduite automobile, Revue de Statistique Appliquée, LIV (4), 39-58, 2006
  5. K.-L. Du and M.N.S. Swamy. Neural Networks in a Softcomputing Framework. Springer, 2006.
  6. N. Hernández, R. J. Biscay, and I. Talavera. Support vector regression methods for functional data. In Progress in Pattern Recognition, Image Analysis and Applications, Proceedings of the 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007, volume 4756 of Lecture Notes in Computer Science, Valparaiso (Chile), November 2007. Springer.

Conférences internationales avec comité de lecture et publication des actes

• (7) Consistency of Derivative Based Functional Classifiers on Sampled Data. Fabrice Rossi et Nathalie Villa. In Proceedings of XVIth European Symposium on Artificial Neural Networks (ESANN 2008), pages 445-450, Bruges, Belgium. April 2008.
  [ps] [pdf] [bib]
• (6) Classification in Hilbert Spaces with Support Vector Machine. Fabrice Rossi et Nathalie Villa. In Proceedings of the XIth International Symposium on Applied Stochastic Models and Data Analysis (ASMDA 2005), pages 635-642, Brest, France. May 2005.
  [lien éditeur] [ps] [pdf] [bib]
  Cité par +/-

  1. M. Fromont and C. Tuleau, Functional classification with margin conditions, In Proceedings of 19th Annual Conference on Learning Theory (COLT 2006), Lecture Notes in Computer Science Volume 4005/2006, pages 94-108, Pittsburgh, PA, USA, June 22-25, 2006.
  2. C. Park, J.-Y. Koo, S. Kim, I. Sohn, and J. W. Lee. Classification of gene functions using support vector machine for time-course gene expression data. Computational Statistics & Data Analysis, 52(5):2578-2587, January 2008.
• (5) Support Vector Machine For Functional Data Classification. Nathalie Villa et Fabrice Rossi. In Proceedings of XIIIth European Symposium on Artificial Neural Networks (ESANN 2005), pages 467-472, Bruges, Belgium. April 2005.
  [lien éditeur] [ps] [pdf] [bib]
  Cité par +/-

  1. M. Strickert, U. Seiffert, N. Sreenivasulu, W. Weschke, T. Villmann and B. Hammer, Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis, Neurocomputing, 69(7-9):651-659, March 2006.
• (4) A Self Organizing Map for Dissimilarity Data. Aïcha El Golli, Brieuc Conan-Guez et Fabrice Rossi. In Proceedings of IFCS 2004, pages 61-68, Chicago, U.S.A.. July 2004.
  [ps] [pdf] [bib]
  Cité par +/-

  1. M. Martín-Merino and A. Muñoz, A Modification of the SOM Algorithm to Deal with Non-Symmetric Proximities, In Proceedings of the 5th Workshop on Self-Organizing Maps (WSOM 05), pages 569-578, Paris (France), September 2005.
  2. T. Ho-Phuoc and A. Guérin-Dugué. A new adaptation of self-organizing map for dissimilarity data. In A. Sandoval, F.and Prieto, J. Cabestany, and M. Graña, editors, Computational and Ambient Intelligence, Proceedings of 9th International Work-Conference on Artificial Neural Networks (IWANN 2007), volume 4507 of Lecture Notes in Computer Science, pages 219-226, San Sebastián (Spain), June 2007. Springer.
• (3) Clustering Functional Data with the SOM algorithm. Fabrice Rossi, Brieuc Conan-Guez et Aïcha El Golli. In Proceedings of XIIth European Symposium on Artificial Neural Networks (ESANN 2004), pages 305-312, Bruges, Belgium. April 2004.
  [lien éditeur] [ps] [pdf] [bib]
  Cité par +/-

  1. J. A. Lee and M. Verleysen. Generalization of the lp norm for time series and its application to self-organizing maps. In Proceedings of the 5th Workshop on Self-Organizing Maps (WSOM 05), pages 733-740, Paris (France), September 2005.
  2. B. Hammer and T. Villmann. Classification using non standard metrics. In Proceedings of XIIIth European Symposium on Artificial Neural Networks (ESANN 2005), pages 303-316, Bruges (Belgium), April 2005.
  3. Vellido, A. and Lisboa, P.J.G. Functional topographic mapping for robust handling of outliers in brain tumour data. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2005), pages 133-138, Bruges (Belgium), April 2005.
  4. M. Strickert, N. Sreenivasulu, W. Weschke, U. Seiffert, T. Villmann and B. Hammer, Generalized Relevance LVQ with Correlation Measures for Biological Data, Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005), pages 331-338, Bruges (Belgium), April 2005.
  5. A. Vellido, P. J.G. Lisboa and D. Vicente, Robust analysis of MRS brain tumour data using t-GTM, Neurocomputing, 69(7-9):754-768, March 2006.
  6. M. Strickert, U. Seiffert, N. Sreenivasulu, W. Weschke, T. Villmann and B. Hammer, Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis, Neurocomputing, 69(7-9):651-659, March 2006.
  7. L. Ferré and N. Villa. Multilayer perceptron with functional inputs: an inverse regression approach. Scandinavian Journal of Statistics, 33 (4), 807-823, December 2006.
  8. S. Tokushige, H. Yadohisa, and K. Inada. Crisp and fuzzy k-means clustering algorithms for multivariate functional data, Computational Statistics, 22(1):1-16, April 2007.
  9. N. Shimizu and M. Mizuta. Functional clustering and functional principal points. In Proceedings of the 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, volume 4693 of Lecture Notes in Computer Science, Vietri sul Mare (Italy), September 2007. Springer.
  10. S.-H. Huang, H.-R. Ke, and W.-P. Yang. Structure clustering for Chinese patent documents. Expert Systems with Applications, 34(4):2290-2297, May 2008.
• (2) Functional Radial Basis Function Network (FRBFN). Nicolas Delannay, Fabrice Rossi, Brieuc Conan-Guez et Michel Verleysen. In Proceedings of XIIth European Symposium on Artificial Neural Networks (ESANN 2004), pages 313-318, Bruges, Belgium. April 2004.
  [lien éditeur] [ps] [pdf] [bib]
  Cité par +/-

  1. Vellido, A. and Lisboa, P.J.G. Functional topographic mapping for robust handling of outliers in brain tumour data. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2005), pages 133-138, Bruges (Belgium), April 2005.
• (1) Functional Preprocessing for Multilayer Perceptrons. Fabrice Rossi et Brieuc Conan-Guez. In Proceedings of XIIth European Symposium on Artificial Neural Networks (ESANN 2004), pages 319-324, Bruges, Belgium. April 2004.
  [lien éditeur] [ps] [pdf] [bib]

Conférences nationales avec comité de lecture et publication des actes

• (4) Estimation de redondance pour le clustering de variables spectrales. Damien François, Catherine Krier, Fabrice Rossi et Michel Verleysen. In Actes des 10èmes journées Européennes Agro-industrie et Méthodes statistiques (Agrostat 2008), pages 55-61, Louvain-la-Neuve, Belgique. Janvier 2008.
  [pdf] [bib]
• (3) Estimation de redondance conditionnelle par plus proches voisins, application au clustering de variables spectrales. Damien François, Catherine Krier, Fabrice Rossi et Michel Verleysen. In Actes de la conférence Chimiométrie 2007, pages 43-46, Lyon, France. Novembre 2007.
  [pdf] [bib]
• (2) Sélection de groupes de variables spectrales par information mutuelle grâce à une représentation spline. Fabrice Rossi, Damien François, Vincent Wertz et Michel Verleysen. In Actes de la conférence Chimiométrie 2005, pages 52-55, Villeneuve d'Ascq, France. Décembre 2005.
  [pdf] [bib]
• (1) Sélection de variables spectrales par information mutuelle multivariée pour la construction de modèles non-linéaires. Amaury Lendasse, Damien François, Fabrice Rossi, Vincent Wertz et Michel Verleysen. In Actes de la conférence Chimiométrie 2004, pages 44-47, Paris, France. Décembre 2004.
  [pdf] [bib]

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