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Faster Gradient Descent Training of Hidden Markov Models, Using Individual Learning Rate Adaptation

  • Pantelis G. Bagos
  • Theodore D. Liakopoulos
  • Stavros J. Hamodrakas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3264)

Abstract

Hidden Markov Models (HMMs) are probabilistic models, suitable for a wide range of pattern recognition tasks. In this work, we propose a new gradient descent method for Conditional Maximum Likelihood (CML) training of HMMs, which significantly outperforms traditional gradient descent. Instead of using fixed learning rate for every adjustable parameter of the HMM, we propose the use of independent learning rate/step-size adaptation, which has been proved valuable as a strategy in Artificial Neural Networks training. We show here that our approach compared to standard gradient descent performs significantly better. The convergence speed is increased up to five times, while at the same time the training procedure becomes more robust, as tested on ap-plications from molecular biology. This is accomplished without additional computational complexity or the need for parameter tuning.

Keywords

Hide Markov Model Learning Rate Gradient Descent Emission Probability Gradient Descent Method 
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 2004

Authors and Affiliations

  • Pantelis G. Bagos
    • 1
  • Theodore D. Liakopoulos
    • 1
  • Stavros J. Hamodrakas
    • 1
  1. 1.Department of Cell Biology and Biophysics, Faculty of BiologyUniversity of AthensPanepistimiopolis, AthensGreece

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