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Markov Models for Pattern Recognition by Gernot A. Fink
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Markov Models for Pattern Recognition

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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.

Author Biography:

Prof. Dr.-Ing. Gernot A. Fink is Head of the Pattern Recognition Research Group at TU Dortmund University, Dortmund, Germany. His other publications include the Springer title Markov Models for Handwriting Recognition.
Release date NZ
August 27th, 2016
Pages
276
Edition
Softcover reprint of the original 2nd ed. 2014
Audience
  • Professional & Vocational
Illustrations
45 Illustrations, black and white; XIII, 276 p. 45 illus.
Dimensions
155x235x15
ISBN-13
9781447171331
Product ID
25774505

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