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Markov Models for Pattern Recognition: From Theory to Applications

Markov Models for Pattern Recognition: From Theory to Applications
Author: Gernot A. Fink
Publisher: Springer
Category: Book

List Price: $59.95
Buy New: $35.95
You Save: $24.00 (40%)



New (25) Used (5) from $35.95

Avg. Customer Rating: 5.0 out of 5 stars 1 reviews
Sales Rank: 1302705

Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 248
Shipping Weight (lbs): 1.3
Dimensions (in): 9.4 x 6.3 x 0.7

ISBN: 3540717668
Dewey Decimal Number: 006.4
EAN: 9783540717669
ASIN: 3540717668

Publication Date: November 16, 2007
Availability: Usually ships in 1-2 business days
Condition: brand new, factory sealed in plastic

Similar Items:

  • Pattern Recognition and Neural Networks
  • Pattern Recognition and Machine Learning (Information Science and Statistics)
  • Inference in Hidden Markov Models (Springer Series in Statistics)

Editorial Reviews:

Product Description

Markov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - covering Hidden Markov models and Markov chain models - as used for sequential data and presents the techniques necessary to build successful systems for practical applications.

Additionally, the actual use of the technology in the three main application areas of pattern recognition methods based on Markov- Models - namely speech recognition, handwriting recognition, and biological sequence analysis - are demonstrated.




Customer Reviews:

5 out of 5 stars just what I needed!!!   April 8, 2008
 1 out of 1 found this review helpful

Though, at times, it shows that the author is German (I mean, in the construction of statements), and the use of commas is a bit slack, I'm very impressed by the clarity (and also, by the practicality) of the exposition. I have not finished the book yet, but nearing the first half of it, almost every page teaches me, of clarifies me, some useful concept. I've tried my hand at Hidden Markov Models with some classical papers (e.g. Rabiner) and books (e.g. MacDonald and Zucchini) ... I wish I had started here.

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