Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000010328902 | Q327 F56 2014 | Open Access Book | Book | Searching... |
Searching... | 33000000017592 | Q327 F56 2014 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
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.
Author Notes
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 .