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Markov Models for Pattern Recognition: From Theory to Applications de Gernot A. Fink

Markov Models for Pattern Recognition: From Theory to Applications: Advances in Computer Vision and Pattern Recognition

Autor Gernot A. Fink
en Limba Engleză Hardback – 28 ian 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.
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Specificații

ISBN-13: 9781447163077
ISBN-10: 1447163079
Pagini: 292
Ilustrații: XIII, 276 p. 45 illus.
Dimensiuni: 155 x 235 x 21 mm
Greutate: 0.59 kg
Ediția:2nd ed. 2014
Editura: SPRINGER LONDON
Colecția Springer
Seria Advances in Computer Vision and Pattern Recognition

Locul publicării:London, United Kingdom

Public țintă

Research

Cuprins

Introduction.- Application Areas.- Part I: Theory.- Foundations of Mathematical Statistics.- Vector Quantization and Mixture Estimation.- Hidden Markov Models.- N-Gram Models.- Part II: Practice.- Computations with Probabilities.- Configuration of Hidden Markov Models.- Robust Parameter Estimation.- Efficient Model Evaluation.- Model Adaptation.- Integrated Search Methods.- Part III: Systems.- Speech Recognition.- Handwriting Recognition.- Analysis of Biological Sequences.

Recenzii

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)

Notă biografică

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.

Textul de pe ultima copertă

Markov models are extremely useful as a general, widely applicable tool for many areas in statistical pattern recognition.
This unique text/reference places the formalism of Markov chain and hidden Markov models at the very center of its examination of current pattern recognition systems, demonstrating how the models can be used in a range of different applications. Thoroughly revised and expanded, this 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.
Topics and features:
  • Introduces the formal framework for Markov models, describing hidden Markov models and Markov chain models, also known as n-gram models
  • Covers the robust handling of probability quantities, which are omnipresent when dealing with these statistical methods
  • Presents methods for the configuration of hidden Markov models for specific application areas, explaining the estimation of the model parameters
  • 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 in automatic speech recognition, character and handwriting recognition, and the analysis of biological sequences
Researchers, practitioners, and graduate students of pattern recognition will all find this book to be invaluable in aiding their understanding of the application of statistical methods in this area.

Caracteristici

Thoroughly revised, updated and expanded new edition Examines pattern recognition systems from the perspective of Markov models, demonstrating how the models can be used in a range of applications Places special emphasis on practical algorithmic solutions Includes supplementary material: sn.pub/extras