Iterative Refinement of HMM and HCRF for Sequence Classification

  • Yann Soullard
  • Thierry Artieres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7081)


We propose a strategy for semi-supervised learning of Hidden-state Conditional Random Fields (HCRF) for signal classification. It builds on simple procedures for semi-supervised learning of Hidden Markov Models (HMM) and on strategies for learning a HCRF from a trained HMM system. The algorithm learns a generative system based on Hidden Markov models and a discriminative one based on HCRFs where each model is refined by the other in an iterative framework.


Unlabeled Data Discriminative Model Iterative Framework Hide Conditional Random Field Structure Output Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yann Soullard
    • 1
  • Thierry Artieres
    • 1
  1. 1.LIP6Pierre and Marie Curie UniversityParisFrance

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