A new HMM-based ensemble generation method for numeral recognition

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A new HMM-based ensemble generation method for numeral recognition

Ko, Albert Hung Ren and Sabourin, Robert and De Souza Britto, Alceu

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2007

Abstract : A new scheme for the optimization of codebook sizes for 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. © Springer-Verlag Berlin Heidelberg 2007.

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