On the structure of hidden Markov models

  • Authors:
  • K. T. Abou-Moustafa;M. Cheriet;C. Y. Suen

  • Affiliations:
  • Dept. of Comp. Sci., CENPARMI, Concordia University, GM-606, Montréal, QC, Canada H3G 1M8 and LIVIA, École de Technologie Supérieure, Univ. de Quebéc, Montréal QC, Canada ...;LIVIA, École de Technologie Supérieure, Univ. de Quebéc, 1100 Notre-Dame W., Montréal QC, Canada H3C 1K3;Department of Computer Science, CENPARMI, Concordia University, GM-606, 1455 de Maisonneuve W., Montréal, QC, Canada H3G 1M8

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2004

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Abstract

This paper investigates the effect of HMM structure on the performance of HMM-based classifiers. The investigation is based on the framework of graphical models, the diffusion of credits of HMMs and empirical experiments. Although some researchers have focused on determining the number of states, this study shows that the topology has a stronger influence on increasing the performance of HMM-based classifiers than the number of states.