Markov Models for Pattern Recognition: From Theory to Applications eBook

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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.

This is a digital product.

Markov Models for Pattern Recognition: From Theory to Applications 2nd Edition is written by Gernot A. Fink and published by Springer. The Digital and eTextbook ISBNs for Markov Models for Pattern Recognition are 9781447163084, 1447163087 and the print ISBNs are 9781447163077, 1447163079.