Markov Models for Pattern Recognition

From Theory to Applications

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Application Software, Computer Graphics, General Computing
Cover of the book Markov Models for Pattern Recognition by Gernot A. Fink, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Gernot A. Fink ISBN: 9781447163084
Publisher: Springer London Publication: January 14, 2014
Imprint: Springer Language: English
Author: Gernot A. Fink
ISBN: 9781447163084
Publisher: Springer London
Publication: January 14, 2014
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer London

Cover of the book 3D Video and Its Applications by Gernot A. Fink
Cover of the book Case Studies in Control by Gernot A. Fink
Cover of the book Pediatric Metabolic Syndrome by Gernot A. Fink
Cover of the book Applications of Multi-Criteria and Game Theory Approaches by Gernot A. Fink
Cover of the book PID Control in the Third Millennium by Gernot A. Fink
Cover of the book Cognition Beyond the Brain by Gernot A. Fink
Cover of the book Pediatric Critical Care Medicine by Gernot A. Fink
Cover of the book Urogynecology: Evidence-Based Clinical Practice by Gernot A. Fink
Cover of the book Hydrogen Fuel Cells for Road Vehicles by Gernot A. Fink
Cover of the book Energy Efficiency and Renewable Energy Through Nanotechnology by Gernot A. Fink
Cover of the book Neurovascular Imaging by Gernot A. Fink
Cover of the book Autoimmune Diseases by Gernot A. Fink
Cover of the book Reproduction of Tactual Textures by Gernot A. Fink
Cover of the book Data Mining in Large Sets of Complex Data by Gernot A. Fink
Cover of the book Genitourinary Imaging by Gernot A. Fink
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy