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Synopsis
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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À propos de l?auteur
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.
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Markov Models for Pattern Recognition: From Theory to Applications (Advances in Computer Vision and Pattern Recognition)
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Markov Models for Pattern Recognition : From Theory to Applications
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Markov Models for Pattern Recognition : From Theory to Applications
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Springer, 2016
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Markov Models for Pattern Recognition: From Theory to Applications (Advances in Computer Vision and Pattern Recognition)
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Markov Models for Pattern Recognition : From Theory to Applications
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Markov Models for Pattern Recognition : From Theory to Applications
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Markov Models for Pattern Recognition (Paperback)
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Springer London Ltd, England, 2016
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Paperback. Etat : new. Paperback. 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. 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; Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781447171331
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MARKOV MODELS FOR PATTERN RECOGNITION
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SPRINGER, DORDRECHT, 2018
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ISBN 13 : 9781447171331
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Markov Models for Pattern Recognition
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Springer London Aug 2016, 2016
ISBN 10 : 1447171330
ISBN 13 : 9781447171331
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 292 pp. Englisch. N° de réf. du vendeur 9781447171331
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Markov Models for Pattern Recognition: From Theory to Applications (Advances in Computer Vision and Pattern Recognition)
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