Philippe Leray, Professeur des Universités / Université Nantes / LINA UMR 6241 / Equipe DUKe, a animé un Seminar@SystemX le 19 janvier à l’IRT SystemX sur le thème « Advances in Learning with Bayesian Networks ».
Résumé
Bayesian networks (BNs) are a powerful tool for graphical representation of the underlying knowledge in the data and reasoning with incomplete or imprecise observations. BNs have been extended (or generalized) in several ways, as for instance, causal BNs, dynamic BNs, Relational BNs, … In this talk, we will focus on Bayesian network learning. BN learning can differ with respect to the task : generative model versus discriminative one ? Then, the learning task can also differ w.r.t the nature of the data : complete data, incomplete data, non i.i.d data, number of variables number of samples, data stream, presence of prior knowledge …Given the diversity of these problems, many approaches have emerged in the literature. I will present a brief panorama of those algorithms and describe our current works in this field.
Biographie
Philippe Leray graduated from a French engineering school in Computer Sciences in 1993. He also got a Ph.D. (Computer Sciences) from the University Paris 6 in 1998, about the use of Bayesian and neural networks for complex system diagnosis. Since 2007, He is a full professor at Polytech’Nantes, a pluridisciplinary French Engineering University. He has been working more intensively on Bayesian networks field for the past 15 years with interests for theory (Bayesian network structure learning, causality) and application (reliability, intrusion detection, bio-informatics). He is also the head of the DUKe (Data User Knowledge) research group, in the Nantes Computer Science lab.
