Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers
| Title | Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers |
| Publication Type | Conference Papers |
| Year of Publication | 2004 |
| Authors | Kang H-J, Doermann D |
| Conference Name | Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on |
| Date Published | 2004/08// |
| Keywords | approximation;, 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. |
| DOI | 10.1109/ICPR.2004.1334071 |