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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.
| Autorius: | Gernot A. Fink |
| Leidėjas: | Springer London |
| Išleidimo metai: | 2016 |
| Knygos puslapių skaičius: | 292 |
| ISBN-10: | 1447171330 |
| ISBN-13: | 9781447171331 |
| Formatas: | 235 x 155 x 16 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Markov Models for Pattern Recognition: From Theory to Applications“
