Markov Models for Pattern Recognition: From Theory to Applications

By Gernot A Fink

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.

• Format: Hardcover
• Language: English
• ISBN: 1447163079
• ISBN13: 9781447163077
• Release Date: January 2014
• Publisher: Springer
• Length: 276 Pages
• Weight: 1.29 lbs.
• Dimensions: 0.7" x 6.1" x 9.2"