Markov Models for Pattern Recognition: From Theory to Applications

✍ Scribed by Gernot A. Fink (auth.)

Publisher: Springer-Verlag London
Year: 2014
Tongue: English
Leaves: 275
Series: Advances in Computer Vision and Pattern Recognition
Edition: 2
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.

✦ Table of Contents

Front Matter....Pages I-XIII
Introduction....Pages 1-7
Application Areas....Pages 9-29
Front Matter....Pages 31-33
Foundations of Mathematical Statistics....Pages 35-49
Vector Quantization and Mixture Estimation....Pages 51-69
Hidden Markov Models....Pages 71-106
n -Gram Models....Pages 107-127
Front Matter....Pages 129-132
Computations with Probabilities....Pages 133-141
Configuration of Hidden Markov Models....Pages 143-152
Robust Parameter Estimation....Pages 153-182
Efficient Model Evaluation....Pages 183-200
Model Adaptation....Pages 201-209
Integrated Search Methods....Pages 211-224
Front Matter....Pages 225-228
Speech Recognition....Pages 229-236
Handwriting Recognition....Pages 237-248
Analysis of Biological Sequences....Pages 249-253
Back Matter....Pages 255-276

✦ Subjects

Pattern Recognition; Image Processing and Computer Vision; Language Translation and Linguistics; Artificial Intelligence (incl. Robotics)

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