Description: Markov Models for Pattern Recognition Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). From Theory to Applications Author(s): Gernot A. Fink Format: Hardback Publisher: Springer London Ltd, United Kingdom Imprint: Springer London Ltd ISBN-13: 9781447163077, 978-1447163077 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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End Time: 2024-11-27T17:01:25.000Z
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Book Title: Markov Models for Pattern Recognition
Number of Pages: 276 Pages
Language: English
Publication Name: Markov Models for Pattern Recognition: from Theory to Applications
Publisher: Springer London LTD
Publication Year: 2014
Subject: Computer Science
Item Height: 235 mm
Item Weight: 606 g
Type: Textbook
Author: Gernot A. Fink
Series: Advances in Computer Vision and Pattern Recognition
Item Width: 155 mm
Format: Hardcover
