Markov Models for Pattern Recognition : From Theory to Applications

Gernot A. Fink

ISBN 10: 1447171330 / ISBN 13: 9781447171331

Verlag: Springer London, Springer London, 2016

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - 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.

Bestandsnummer des Verkäufers 9781447171331

Bibliografische Details

• Titel: Markov Models for Pattern Recognition : From Theory to Applications
• Verlag: Springer London, Springer London
• Erscheinungsjahr: 2016
• Sprache: Englisch
• ISBN-10: 1447171330
• ISBN-13: 9781447171331
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.