Markov Models for Pattern Recognition, 2nd Edition

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

Chapter 1: Introduction
Chapter 2: Application Areas

Part I: Theory
Chapter 3: Foundations of Mathematical Statistics
Chapter 4: Vector Quantization and Mixture Estimation
Chapter 5: Hidden Markov Models
Chapter 6: n-Gram Models

Part II: Practice
Chapter 7: Computations with Probabilities
Chapter 8: Configuration of Hidden Markov Models
Chapter 9: Robust Parameter Estimation
Chapter 10: Efficient Model Evaluation
Chapter 11: Model Adaptation
Chapter 12: Integrated Search Methods

Part III: Systems
Chapter 13: Speech Recognition
Chapter 14: Handwriting Recognition
Chapter 15: Analysis of Biological Sequences