Markov Models for Pattern Recognition (2^nd Ed., Softcover reprint of the original 2nd ed. 2014)
From Theory to Applications
Advances in Computer Vision and Pattern Recognition Series

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.

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