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Scaled On-line Unsupervised Learning Algorithm for a SOM-HMM Hybrid

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Computer and Information Sciences II

Abstract

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a scaled on-line gradient descent unsupervised learning algorithm.

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References

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Author information

Authors and Affiliations

  1. Intelligent Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zografou, Athens, Greece

    Christos Ferles, Georgios Siolas & Andreas Stafylopatis

Authors
  1. Christos Ferles
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  2. Georgios Siolas
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  3. Andreas Stafylopatis
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Corresponding author

Correspondence to Christos Ferles .

Editor information

Editors and Affiliations

  1. , Dept of Electrical and Electronics Eng'g, Imperial College, London, SW7 2BT, United Kingdom

    Erol Gelenbe

  2. Imperial College, London, United Kingdom

    Ricardo Lent

  3. University of East London, London, United Kingdom

    Georgia Sakellari

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© 2011 Springer-Verlag London Limited

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Ferles, C., Siolas, G., Stafylopatis, A. (2011). Scaled On-line Unsupervised Learning Algorithm for a SOM-HMM Hybrid. In: Gelenbe, E., Lent, R., Sakellari, G. (eds) Computer and Information Sciences II. Springer, London. https://doi.org/10.1007/978-1-4471-2155-8_68

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  • DOI: https://doi.org/10.1007/978-1-4471-2155-8_68

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2154-1

  • Online ISBN: 978-1-4471-2155-8

  • eBook Packages: EngineeringEngineering (R0)

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