𝔖 Scriptorium
✦   LIBER   ✦

📁

Markov Models for Pattern Recognition: From Theory to Applications

✍ Scribed by Gernot A. Fink


Publisher
Springer
Year
2010
Tongue
English
Leaves
256
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


Markov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - covering Hidden Markov models and Markov chain models - as used for sequential data and presents the techniques necessary to build successful systems for practical applications.Additionally, the actual use of the technology in the three main application areas of pattern recognition methods based on Markov- Models - namely speech recognition, handwriting recognition, and biological sequence analysis - are demonstrated.

✦ Subjects


Информатика и вычислительная техника;Искусственный интеллект;Распознавание образов;


📜 SIMILAR VOLUMES


Markov models for pattern recognition: F
✍ Fink G.A. 📂 Library 📅 2008 🏛 Springer 🌐 English

This comprehensive introduction to the Markov modeling framework describes the underlying theoretical concepts of Markov models as used for sequential data, covering Hidden Markov models and Markov chain models. It also presents the techniques necessary to build successful systems for practical appl

Markov Models for Pattern Recognition: F
✍ Gernot A. Fink 📂 Library 📅 2010 🏛 Springer 🌐 English

This comprehensive introduction to the Markov modeling framework describes the underlying theoretical concepts of Markov models as used for sequential data, covering Hidden Markov models and Markov chain models. It also presents the techniques necessary to build successful systems for practical appl

Markov Models for Pattern Recognition: F
✍ Gernot A. Fink (auth.) 📂 Library 📅 2008 🏛 Springer Berlin Heidelberg 🌐 English

<P>Markov models are used to solve challenging pattern recognition problems<BR>on the basis of sequential data as, e.g., automatic speech or handwriting<BR>recognition. This comprehensive introduction to the Markov modeling framework<BR>describes both the underlying theoretical concepts of Markov mo

Markov Models for Pattern Recognition: F
✍ Gernot A. Fink (auth.) 📂 Library 📅 2014 🏛 Springer-Verlag London 🌐 English

<p>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 <i>n</i>-best search.

From Statistics to Neural Networks: Theo
✍ Jerome H. Friedman (auth.), Vladimir Cherkassky, Jerome H. Friedman, Harry Wechs 📂 Library 📅 1994 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

<p>The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to­ gether over 100 participants (including 19 invited lecturers) from 20 c