Markov models for pattern recognition : from theory to applications
Author(s)
Bibliographic Information
Markov models for pattern recognition : from theory to applications
（Advances in computer vision and pattern recognition / Sameer Singh, Sing Bing Kang, series editors）
Springer, c2014
2nd ed
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Note
Includes bibliographical references (p. 255-271) and index
Description and Table of Contents
Description
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.
Table of Contents
Introduction
Application Areas
Part I: Theory
Foundations of Mathematical Statistics
Vector Quantization and Mixture Estimation
Hidden Markov Models
n-Gram Models
Part II: Practice
Computations with Probabilities
Configuration of Hidden Markov Models
Robust Parameter Estimation
Efficient Model Evaluation
Model Adaptation
Integrated Search Methods
Part III: Systems
Speech Recognition
Handwriting Recognition
Analysis of Biological Sequences
by "Nielsen BookData"
