Markov Models for Pattern Recognition

From Theory to Applications

By Gernot A. Fink

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This introduction to the Markov modeling framework describes the underlying theoretical concepts of Markov models as used for sequential data, covering Hidden Markov models and Markov chain models. It presents the techniques necessary to build successful systems.

Full Description

  • ISBN13: 978-3-5407-1766-9
  • 260 Pages
  • User Level: Professionals
  • Publication Date: October 17, 2007
  • Available eBook Formats: PDF
  • eBook Price: $69.95
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Full Description
Markov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - covering Hidden Markov models and Markov chain models - as used for sequential data and presents the techniques necessary to build successful systems for practical applications. Additionally, the actual use of the technology in the three main application areas of pattern recognition methods based on Markov- Models - namely speech recognition, handwriting recognition, and biological sequence analysis - are demonstrated.
Table of Contents

Table of Contents

  1. Application Areas:
  2. Speech.
  3. Handwriting.
  4. Biological Sequences.
  5. Foundations of Mathematical Statistics.
  6. Vector Quantisation.
  7. Hidden
  8. Markov Models.
  9. n
  10. Gram
  11. Models.
  12. Computations with Probabilities.
  13. Configuration of Hidden
  14. Markov
  15. Models.
  16. Robust Parameter Estimation.
  17. Efficient Model Evaluation.
  18. Model Adaptation.
  19. Integrated Search.
  20. Speech Recognition.
  21. Text Recognition.
  22. Analysis of Biological Sequences.
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