Markov Models for Pattern Recognition

From Theory to Applications

Gernot A. Fink

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English

Springer London Ltd
27 August 2016

Artificial intelligence; Natural language & machine translation; Pattern recognition; Computer vision

Series: Advances in Computer Vision and Pattern Recognition

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.

By: Gernot A. Fink
Imprint: Springer London Ltd
Country of Publication: United Kingdom
Edition: Softcover reprint of the original 2nd ed. 2014
Dimensions: Height: 235mm, Width: 155mm, Spine: 15mm
Weight: 4.453kg
ISBN: 9781447171331
ISBN 10: 1447171330
Series: Advances in Computer Vision and Pattern Recognition
Pages: 276
Publication Date: 27 August 2016
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.

Reviews for Markov Models for Pattern Recognition: From Theory to Applications

From the book reviews: “The book is highly appropriate for researchers and practitioners dealing with pattern recognition in general and speech, character and handwriting recognition sequences, in particular.” (Catalin Stoean, zbMATH 1307.68001, 2015)