科研组 : MALIRE - MAchine Learning and Information REtrieval
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The main activities of the MALIRE team (Machine Learning and Information Retrieval) are based on artificial intelligence methods, and more specifically on theoretical and algorithmic aspects of machine learning. Its members are specialized in statistical learning, neural networks and probabilistic methods, as well as fuzzy logic and management of uncertainty in intelligent systems. Beside these fundamental research topics of interest, the team has tackled three important domains of application, namely text and multimedia information retrieval, complex data mining and risk forecasting and eventually user modelling and personalization of man-machine interactions.
The topics of research of the MALIRE team are organized in five main non disjoint streams. The first one corresponds to theoretical foundations of machine learning, sequence analysis, management of structured data and inductive learning. The second one is devoted to the study of similarities and their cognitive foundations. The three other streams are focused on the domains of application we have described. MALIRE has various interactions with cognitive science and usage mining.
- V. Vu, N. Labroche, B. Bouchon‑Meunier : “Improving Constrained Clustering with Active Query”, Pattern Recognition, vol. 45 (4), pp. 1749-1758, (Elsevier) [Vu 2012]
- E. Hullermeier, M. Rifqi, S. Henzgen, R. Senge : “Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures”, IEEE Transactions on Fuzzy Systems, vol. 20 (3), pp. 546-556, (Institute of Electrical and Electronics Engineers) [Hullermeier 2012]
- T. Do, Th. Artières : “Large Margin Training for Hidden Markov Models with Partially Observed States”, International Conference on Machine Learning (ICML), Montreal, Canada, pp. 265-272, (ACM) [Do 2009a]
- D. Buffoni, C. Calauzènes, P. Gallinari, N. Usunier : “Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision”, The 28th International Conference on Machine Learning (ICML 2011), Bellevue, WA, United States, pp. 825-832 [Buffoni 2011a]
- F. Maes, L. Denoyer, P. Gallinari : “Structured Prediction with Reinforcement Learning”, Machine Learning, vol. 77 (2-3), pp. 271-301, (Springer Verlag) [Maes 2009a]
- N. Thome, S. Miguet, S. Ambellouis : “A Real-Time, Multi-View Fall Detection System: a LHMM-Based Approach”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 18 (11), pp. 1522-1532, (Institute of Electrical and Electronics Engineers) [Thome 2008b]
- D. Gorisse, M. Cord, F. Precioso : “Locality sensitive hashing for chi2 distance”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34 (2), pp. 402-410, (Institute of Electrical and Electronics Engineers) [Gorisse 2012]
- B. Bouchon‑Meunier, A. Laurent, M.‑J. Lesot, M. Rifqi : “Strengthening fuzzy gradual rules through "all the more" clauses”, IEEE International Conference on Fuzzy Systems Fuzz-IEEE'10 (WCCI'2010), Barcelona, Spain, pp. 2940-2946, (IEEE) [Bouchon-Meunier 2010a]
- M. Detyniecki, Ch. Marsala, M. Rifqi : “Double-linear fuzzy interpolation method”, IEEE International Conference on Fuzzy Systems FUZZIEEE'2011, Taipei, Taiwan, Province of China, pp. 455-462 [Detyniecki 2011b]
- M.‑R. Amini, C. Goutte : “A Co-classification Approach to Learning from Multilingual Corpora”, Machine Learning, vol. 79 (1-2), pp. 105-121, (Springer Verlag) [Amini 2010a]