Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers

TitleProduct approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers
Publication TypeConference Papers
Year of Publication2004
AuthorsKang H-J, Doermann D
Conference NamePattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Date Published2004/08//
Keywordsapproximation;, Bayes, Bayesian, bound, character, classification;, classifiers;, conditional, distribution;, entropy;, error, formalism;, handwritten, methods;, minimisation;, multiple, numerals;, pattern, probability, probability;, Product, rate;, recognition;, statistics;, unconstrained, upper
Abstract

In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.

DOI10.1109/ICPR.2004.1334071