Articoli correlati a Markov Models for Pattern Recognition: From Theory...
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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.
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Informazioni sull?autore
Gernot A. Fink earned his diploma in computer science from the
University of Erlangen-Nuremberg, Erlangen, Germany, in 1991.
He recieved a Ph.D. degree in computer science in 1995 and
the venia legendi in applied computer science in 2002 both
from Bielefeld University, Germany.
Currently, he is professor for Pattern Recognition in Embedded Systems
at the University of Dortmund, Germany, where he also heads the
Intelligent Systems Group at the Robotics Research Institute.
His reserach interests lie in the development and application of
pattern recognition methods in the fields of man machine interaction,
multimodal machine perception including speech and image processing,
statistical pattern recognition, handwriting recognition, and the
analysis of genomic data.
Dalla quarta di copertina
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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