ENGLISH

Markov Models for Pattern Recognition: From Theory to Applications

By Gernot A. Fink (auth.)

Book information

Publisher
Springer-Verlag London
Year
2014
ISBN
978-1-4471-6307-7, 978-1-4471-6308-4
DOI
10.1007/978-1-4471-6308-4
Language
english
Format
PDF
Filesize
4 MB (3983610 bytes)
Series
Advances in Computer Vision and Pattern Recognition
Edition
2
Pages
276\275
Time added
2014-02-12 18:00:00
Pattern Recognition; Image Processing and Computer

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.

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