- Hardcover: 248 pages
- Publisher: Springer; 2008 edition (October 2, 2007)
- Language: English
- ISBN-10: 3540717668
- ISBN-13: 978-3540717669
- Product Dimensions: 6.9 x 0.8 x 9.4 inches
- Shipping Weight: 1.3 pounds
- Average Customer Review: 4 customer reviews
- Amazon Best Sellers Rank: #4,049,935 in Books (See Top 100 in Books)
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Markov Models for Pattern Recognition: From Theory to Applications 2008th Edition
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"The practice part makes the book unique among many other pattern recognition textbooks. It discusses implementation details that are often ignored in the literature, but are important in constructing a working system. … Overall, the book is well written and clear ... It is suited not to those who want to learn the basics of pattern recognition, but to those who want to learn the state of the art of speech, character, and DNA sequence recognition problems from the perspective of the practitioner and designer. … The depth and breadth of the treatment is right for the intent of the book."
(T. Kubota, Lewisburg, PA, in: Computing Reviews, May 2009)
From the Back Cover
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
This comprehensive introduction to the Markov modeling framework describes the underlying theoretical concepts - covering Hidden Markov models and Markov chain models - and presents the techniques and algorithmic solutions essential to creating real world applications. The actual use of Markov models in their three main application areas - namely speech recognition, handwriting recognition, and biological sequence analysis - is presented with examples of successful systems.
Encompassing both Markov model theory and practise, this book addresses the needs of practitioners and researchers from the field of pattern recognition as well as graduate students with a related major field of study.
Top customer reviews
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Kidding aside, the language in this book is convoluted, obtuse and obviously German lightly translated. I am still working on the introductory statistics section, but I find it easier to read about the concepts mentioned elsewhere online, like on wikipedia. The issues are confused, unnecessarily general, pedantic and overly mathematical. I am a very good programmer but not super mathematical so a more algorithmic approach to the subject would have been better for me. I'll update my review when I get the applied part of the book, but the applied sections don't seem to let up on the mathematical notation. Pseudo code or C or any kind of algorithmic description would have been appreciated.