PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Structure learning with nonparametric decomposable models
Anton Schwaighofer, Mathaeus Dejori, Volker Tresp and Martin Stetter
In: Proceedings of ICANN 2007 (2007) Springer , pp. 119-128.

Abstract

We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete and continuous distributions can be handled in a unified framework. Also, consistency of the underlying probabilistic model is guaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow fast greedy forward and backward selection within the class of decomposable models. We show the validity of this structure learning approach on toy data, and on two large sets of gene expression data.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3352
Deposited By:Anton Schwaighofer
Deposited On:08 February 2008