A new HMM-based ensemble generation method for numeral recognition

Authors:
Albert Hung-Ren Ko;Robert Sabourin;Alceu De Souza Britto, Jr.
Affiliations:
LIVIA, ETS, University of Quebec, Montreal, Quebec, Canada and PPGIA, Pontifical Catholic University of Parana, Curitiba, Brazil;LIVIA, ETS, University of Quebec, Montreal, Quebec, Canada and PPGIA, Pontifical Catholic University of Parana, Curitiba, Brazil;LIVIA, ETS, University of Quebec, Montreal, Quebec, Canada and PPGIA, Pontifical Catholic University of Parana, Curitiba, Brazil
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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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.