Ensemble of HMM classifiers based on the clustering validity index for a handwritten numeral recognizer.

Autor:	Albert Ko, Robert Sabourin, Alceu Britto
Předmět:	HIDDEN Markov models MARKOV processes STOCHASTIC processes EXCESSIVE measures (Mathematics)
Zdroj:	Pattern Analysis & Applications; Feb2009, Vol. 12 Issue 1, p21-35, 15p
Abstrakt:	Abstract  A new scheme for the optimization of codebook sizes for Hidden Markov Models (HMMs) and the generation of HMM ensembles is proposed in this paper. In a discrete HMM, the vector quantization procedure and the generated codebook are associated with performance degradation. By using a selected clustering validity index, we show that the optimization of HMM codebook size can be selected without training HMM classifiers. Moreover, the proposed scheme yields multiple optimized HMM classifiers, and each individual HMM is based on a different codebook size. By using these to construct an ensemble of HMM classifiers, this scheme can compensate for the degradation of a discrete HMM. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink