PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Conditional Graphical Models
Fernando Perez-Cruz, Massimiliano Pontil and Zoubin Ghahramani
In: Predicting Structured Data (2007) MIT Press , Cambridge, MA (USA) , pp. 265-282. ISBN 0-262-02617-1


In this chapter we propose a modification of CRF-like algorithms that allows for solving large-scale structured classification problems. Our approach consists in upper bounding the CRF functional in order to decompose its training into independent optimisation problems per clique. Furthermore we show that each sub-problem corresponds to solving a multiclass learning task in each clique, which enlarges the applicability of these tools for large-scale structural learning problems. Before presenting the Conditional Graphical Model (CGM), as we refer to this procedure, we review the family of CRF algorithms. We concentrate on the best known procedures and standard generalisations of CRFs. The objective of this introduction is analysing from the same viewpoint the proposed solutions in the literature to tackle this problem, which allows comparing their different features. We complete the chapter with a case study, in which we show the possibility to work with large-scale problems using CGM and that the obtained performance is comparable to the result with CRF-like algorithms.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4901
Deposited By:Fernando Perez-Cruz
Deposited On:24 March 2009