1.1 Dimension reduction based on finite mixture modeling of inverse regression (Luca Scrucca)
Consider the usual regression problem in which we want to study the conditional distribution of a response Y given a set of predictors X. Sufficient d...
StatLearn 2012 - Workshop on "Challenging problems in Statistical Learning"
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About
L'apprentissage statistique joue de nos jours un rôle croissant dans de nombreux domaines scientifiques et doit de ce fait faire face à des problèmes nouveaux. Il est par conséquent important de proposer des méthodes d'apprentissage statistique adaptées aux problèmes modernes posés par les différents champs d'application. Outre l'importance de la précision des méthodes proposées, elles devront également apporter une meilleure compréhension des phénomènes observés. Afin de faciliter les contacts entre les différentes communautés et de faire ainsi germer de nouvelles idées, un colloquium d'audience internationale (en langue anglaise) sur le thème «Challenging problems in Statistical Learning» a été organisé à l'Université Paris 1 les 5 et 6 avril 2012. Vous trouverez ci-dessous les enregistrements des exposés donnés lors de ce colloquium. Ce colloquium a été organisé par C. Bouveyron, Christophe Biernacki , Alain Célisse , Serge Iovle & Julien Jacques (Laboratoire SAMM, Paris 1, Laboratoire Paul Painlevé, Université Lille 1, CNRS & Modal, INRIA), avec le soutien de la SFdS. Recommandé à : étudiant de la discipline, chercheur - Catégorie : cours podcast - Année de réalisation : 2012
1.2 Information Visualization: An Introduction to the Field and Applications for Statistics (Petra Isenberg)
Information visualization is a research area that focuses on making structures and content of large and complex data sets visually understandable and ...
2.1 Hypothesis Testing and Bayesian Inference: New Applications of Kernel Methods (Arthur Gretton)
In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear learning algorithms from linea...
2.2 Functional estimation in high dimensional data : Application to classification (Sophie Dabo-Niang)
Functional data are becoming increasingly common in a variety of fields. Many studies underline the importance to consider the representation of data ...
2.3 Discriminative clustering for high-dimensional data (Camille Brunet)
A new family of 12 probabilistic models, introduced recently, aims to simultaneously cluster and visualize high-dimensional data. It is based on a mix...
3.1 Exploring Clustering Structure in Ranking Data (Brendan Murphy)
Cluster analysis is concerned with finding homogeneous groups in a population. Model-based clustering methods provide a framework for developing clust...
3.2 Co-clustering under different approaches (Mohamed Nadif)
Cluster analysis is an important tool in a variety of scientific areas including pattern recognition, document clustering, and the analysis of microar...
3.3 Complexity control in overlapping stochastic block models (Pierre Latouche)
Networks are highly used to represent complex systems as sets of interactions between units of interest. For instance, regulatory networks can describ...
4.1 Data-driven penalties: heuristics, results and thoughts... (Pascal Massart)
The idea of selecting a model via penalizing a log-likelihood type criterion goes back to the early seventies with the pioneering works of Mallows and...
4.2 A sliced inverse regression approach for block-wise evolving data streams (Jérôme Saracco)
In this communication, we focus on data arriving sequentially by block in a stream. A semiparametric regression model involving a common EDR (Effectiv...