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Markov Models for Pattern Recognition

From Theory to Applications

  • Textbook
  • © 2014

Overview

Authors:
  1. Gernot A. Fink

    1. Department of Computer Science, Technical University of Dortmund, Dortmund, Germany

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  • Thoroughly revised, updated and expanded new edition
  • Examines pattern recognition systems from the perspective of Markov models, demonstrating how the models can be used in a range of applications
  • Places special emphasis on practical algorithmic solutions
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XIII
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  2. Introduction

    • Gernot A. Fink
    Pages 1-7

  3. Application Areas

    • Gernot A. Fink
    Pages 9-29

  4. Theory

    1. Front Matter

      Pages 31-33
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    2. Foundations of Mathematical Statistics

      • Gernot A. Fink
      Pages 35-49

    3. Vector Quantization and Mixture Estimation

      • Gernot A. Fink
      Pages 51-69

    4. Hidden Markov Models

      • Gernot A. Fink
      Pages 71-106

    5. n-Gram Models

      • Gernot A. Fink
      Pages 107-127

  5. Practice

    1. Front Matter

      Pages 129-132
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    2. Computations with Probabilities

      • Gernot A. Fink
      Pages 133-141

    3. Configuration of Hidden Markov Models

      • Gernot A. Fink
      Pages 143-152

    4. Robust Parameter Estimation

      • Gernot A. Fink
      Pages 153-182

    5. Efficient Model Evaluation

      • Gernot A. Fink
      Pages 183-200

    6. Model Adaptation

      • Gernot A. Fink
      Pages 201-209

    7. Integrated Search Methods

      • Gernot A. Fink
      Pages 211-224

  6. Systems

    1. Front Matter

      Pages 225-228
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    2. Speech Recognition

      • Gernot A. Fink
      Pages 229-236

    3. Handwriting Recognition

      • Gernot A. Fink
      Pages 237-248

    4. Analysis of Biological Sequences

      • Gernot A. Fink
      Pages 249-253

  7. Back Matter

    Pages 255-276
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Keywords

About this book

This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.

Reviews

From the book reviews:

“The book is highly appropriate for researchers and practitioners dealing with pattern recognition in general and speech, character and handwriting recognition sequences, in particular.” (Catalin Stoean, zbMATH 1307.68001, 2015)

Authors and Affiliations

  • Department of Computer Science, Technical University of Dortmund, Dortmund, Germany

    Gernot A. Fink

About the author

Prof. Dr.-Ing. Gernot A. Fink is Head of the Pattern Recognition Research Group at TU Dortmund University, Dortmund, Germany. His other publications include the Springer title Markov Models for Handwriting Recognition.

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