Markov Models for Pattern Recognition - From Theory to Applications (Paperback, Softcover reprint of the original 2nd ed. 2014)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Advances in Computer Vision and Pattern Recognition
Release date:	August 2016
First published:	2014
Authors:	Gernot A. Fink
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Paperback
Pages:	276
Edition:	Softcover reprint of the original 2nd ed. 2014
ISBN-13:	978-1-4471-7133-1
Categories:	Books > Computing & IT > General Promotions
LSN:	1-4471-7133-0
Barcode:	9781447171331

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