Item description for Markov Models for Pattern Recognition: From Theory to Applications by Gernot A. Fink...
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.
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Est. Packaging Dimensions: Length: 9.37" Width: 6.3" Height: 0.71" Weight: 1.32 lbs.
Release Date Nov 16, 2007
ISBN 3540717668 ISBN13 9783540717669
Availability 103 units. Availability accurate as of May 28, 2017 10:03.
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More About Gernot A. Fink
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.
Reviews - What do customers think about Markov Models for Pattern Recognition: From Theory to Applications?
just what I needed!!! Apr 8, 2008
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.