Markov Models for Pattern Recognition

Beschreibung

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.

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.

Markov models are used to solve challenging pattern recognition problems on the basis of sequential data, such as automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes the underlying theoretical concepts of Markov models as used for sequential data, covering Hidden Markov models and Markov chain models. It also presents the techniques necessary to build successful systems for practical applications. In addition, the book demonstrates the actual use of the technology in the three main application areas of pattern recognition methods based on Markov-Models: speech recognition, handwriting recognition, and biological sequence analysis. The book is suitable for experts as well as for practitioners.

Autor*in

Gernot A. Fink

Themen in »Markov Models for Pattern Recognition«

Biological Sequence Analysis Hidden Markov Model Markov Model Markov-Models Pattern Recognition algorithms cognition handwriting recognition modeling optimization speech recognition

Stimmen zu »Markov Models for Pattern Recognition«

"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."

Details

Link teilen