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Markov Models for Pattern Recognition [electronic resource] :From Theory to Applications / by Gernot A. Fink.

by Fink, Gernot A [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: XII, 248 p. 51 illus. online resource.ISBN: 9783540717706.Subject(s): Computer science | Artificial intelligence | Translators (Computer programs) | Computer vision | Optical pattern recognition | Computer Science | Pattern Recognition | Image Processing and Computer Vision | Language Translation and Linguistics | Artificial Intelligence (incl. Robotics)DDC classification: 006.4 Online resources: Click here to access online
Contents:
Application Areas -- Application Areas -- Theory -- Foundations of Mathematical Statistics -- Vector Quantization -- Hidden Markov Models -- n-Gram Models -- Practice -- Computations with Probabilities -- Configuration of Hidden Markov Models -- Robust Parameter Estimation -- Efficient Model Evaluation -- Model Adaptation -- Integrated Search Methods -- Systems -- Speech Recognition -- Character and Handwriting Recognition -- Analysis of Biological Sequences.
In: Springer eBooksSummary: 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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Application Areas -- Application Areas -- Theory -- Foundations of Mathematical Statistics -- Vector Quantization -- Hidden Markov Models -- n-Gram Models -- Practice -- Computations with Probabilities -- Configuration of Hidden Markov Models -- Robust Parameter Estimation -- Efficient Model Evaluation -- Model Adaptation -- Integrated Search Methods -- Systems -- Speech Recognition -- Character and Handwriting Recognition -- Analysis of Biological Sequences.

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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