Markov models for pattern recognition: From theory to applications by Fink G.A.

Markov models for pattern recognition: From theory to applications



Download Markov models for pattern recognition: From theory to applications




Markov models for pattern recognition: From theory to applications Fink G.A.
Language: English
Page: 261
Format: pdf
ISBN: 3642090885, 9783642090882
Publisher: Springer

Review

"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 applications. 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.

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