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Partially-Hidden Markov Models

  • Emmanuel Ramasso
  • Thierry Denœux
  • Noureddine Zerhouni
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft” labels represent partial knowledge about the possible states at each time step and the “softness” is encoded by belief functions. For the obtained model, called a Partially-Hidden Markov Model (PHMM), the training algorithm is based on the Evidential Expectation-Maximisation (E2M) algorithm. The usual HMM model is recovered when the belief functions are vacuous and the obtained model includes supervised, unsupervised and semi-supervised learning as special cases.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emmanuel Ramasso
    • 1
  • Thierry Denœux
    • 2
  • Noureddine Zerhouni
    • 1
  1. 1.Automatic Control and Micro-Mechatronic Systems DepartmentFEMTO-ST Institute, UMR CNRS 6174 - UFC / ENSMM / UTBMBesançonFrance
  2. 2.Heudiasyc, U.M.R. C.N.R.S. 7253, Centre de Recherches de RoyallieuUniversité de Technologie de CompiègneCompiègne CedexFrance

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