Seminar 29/10@C48 : Albert Thomas / François Portier

Jeudi 29 Octobre 2015, salle C48

14h – 15h : Albert Thomas (doctorant, LTCI, Telecom ParisTech)

Titre : Calibration of One-Class SVM for Minimum Volume set estimation” Abstract: A general approach for anomaly detection or novelty detection consists in estimating high density regions or Minimum Volume (MV) sets. The One-Class Support Vector Machine (OCSVM) is a state-of-the-art algorithm for estimating such regions from high dimensional data. Yet it suffers from practical limitations. When applied to a limited number of samples it can lead to poor performance even when picking the best hyperparameters. Moreover the solution of OCSVM is very sensitive to the selection of hyperparameters which makes it hard to optimize in an unsupervised setting. After briefly introducing the context of anomaly detection and MV sets estimation I will present a new approach to estimate MV sets using the OCSVM. Such an approach makes it possible to tune the hyperparameters automatically and experimental results show that it outperforms the standard OCSVM.(Joint work with Vincent Feuillard and Alexandre Gramfort)

15h – 16h : François Portier (post-doctorant, ISBA , Univ. Catholique de Louvain)

Titre : Integral approximation by kernel smoothing Abstract: We introduce a new stochastic procedure for integral approximation. Given a real valued function and some points randomly distributed over a compact set of the Euclidean space, the algorithm returns an accurate approximation of the integral of the function over the compact set. The main ingredient of the method is the evaluation of the classical kernel estimator associated to the points. This quantity captures the isolation of the points. In a theoretical part, we give bounds on the rate of convergence and we describe the limiting distribution. Then we debate the choice of the bandwidth for the kernel estimator and we highlight the good behavior of the procedure through simulations. (Joint work with R. Azais and B. Delyon)

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