Markov Models for Pattern Recognition

From Theory to Applications
2. Auflage 2014
ISBN: 978-1-4471-6307-7
Verlag: Springer

Buch, Englisch, 276 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 606 g

Reihe: Advances in Computer Vision and Pattern Recognition

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.

Zielgruppe

Research

Autoren/Hrsg.

Fink, Gernot A.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Introduction.- Application Areas.- Part I: Theory.- Foundations of Mathematical Statistics.- Vector Quantization and Mixture Estimation.- Hidden Markov Models.- N-Gram Models.- Part II: Practice.- Computations with Probabilities.- Configuration of Hidden Markov Models.- Robust Parameter Estimation.- Efficient Model Evaluation.- Model Adaptation.- Integrated Search Methods.- Part III: Systems.- Speech Recognition.- Handwriting Recognition.- Analysis of Biological Sequences.

Über Autor(innen)

Prof. Dr.-Ing. Gernot A. Fink is Head of the Pattern Recognition Research Group at TU Dortmund University, Dortmund, Germany. His other publications include the Springer title Markov Models for Handwriting Recognition.