Ebook: Markov Models for Pattern Recognition: From Theory to Applications
Author: Gernot A. Fink (auth.)
- Tags: Pattern Recognition, Image Processing and Computer Vision, Language Translation and Linguistics, Artificial Intelligence (incl. Robotics)
- Year: 2008
- Publisher: Springer Berlin Heidelberg
- Edition: Original German edition published by Teubner, 20032008
- Language: English
- pdf
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.
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.
Content:
Front Matter....Pages I-XII
Introduction....Pages 1-6
Application Areas....Pages 7-27
Front Matter....Pages 29-31
Foundations of Mathematical Statistics....Pages 33-44
Vector Quantization....Pages 45-59
Hidden Markov Models....Pages 61-93
n-Gram Models....Pages 95-113
Front Matter....Pages 115-118
Computations with Probabilities....Pages 119-125
Configuration of Hidden Markov Models....Pages 127-136
Robust Parameter Estimation....Pages 137-164
Efficient Model Evaluation....Pages 165-179
Model Adaptation....Pages 181-188
Integrated Search Methods....Pages 189-201
Front Matter....Pages 203-206
Speech Recognition....Pages 207-214
Character and Handwriting Recognition....Pages 215-220
Analysis of Biological Sequences....Pages 221-225
Back Matter....Pages 227-248
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.
Content:
Front Matter....Pages I-XII
Introduction....Pages 1-6
Application Areas....Pages 7-27
Front Matter....Pages 29-31
Foundations of Mathematical Statistics....Pages 33-44
Vector Quantization....Pages 45-59
Hidden Markov Models....Pages 61-93
n-Gram Models....Pages 95-113
Front Matter....Pages 115-118
Computations with Probabilities....Pages 119-125
Configuration of Hidden Markov Models....Pages 127-136
Robust Parameter Estimation....Pages 137-164
Efficient Model Evaluation....Pages 165-179
Model Adaptation....Pages 181-188
Integrated Search Methods....Pages 189-201
Front Matter....Pages 203-206
Speech Recognition....Pages 207-214
Character and Handwriting Recognition....Pages 215-220
Analysis of Biological Sequences....Pages 221-225
Back Matter....Pages 227-248
....