Mixtures of kikuchi approximations

Authors:
Roberto Santana;Pedro Larrañaga;Jose A. Lozano
Affiliations:
Intelligent System Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastián, Spain;Intelligent System Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastián, Spain;Intelligent System Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastián, Spain
Venue:
ECML'06 Proceedings of the 17th European conference on Machine Learning
Year:
2006

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Abstract

Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.