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 for Pattern Recognition Gernot A. Fink Markov Models for Pattern Recognition From Theory to Applications With 51 Figures 123 Gernot A. Fink Department of Computer Science University of Dortmund Otto-Hahn-Str. 16 44221 Dortmund Germany
[email protected] Library of Congress Control Number: 2007935304 Originally published in the German language by B.G. Teubner Verlag as “Gernot A. Fink: Mustererkennung mit Markov-Modellen”. © B.G. Teubner Verlag | GWV Fachverlage GmbH, Wiesbaden 2003 ISBN 978-3-540-71766-9 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of Septe