Markov models for pattern recognition: from theory to applications

Markov models for pattern recognition: F

📁 Markov models for pattern recognition: From theory to applications

✍ 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

📁 Markov Models for Pattern Recognition: From Theory to Applications

✍ 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

📁 Markov Models for Pattern Recognition: From Theory to Applications

✍ Gernot A. Fink (auth.) 📂 Library 📅 2008 🏛 Springer Berlin Heidelberg 🌐 English

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 mo

Markov Models for Pattern Recognition: F

📁 Markov Models for Pattern Recognition: From Theory to Applications

✍ Gernot A. Fink (auth.) 📂 Library 📅 2014 🏛 Springer-Verlag London 🌐 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.

Markov Models for Pattern Recognition: F

📁 Markov Models for Pattern Recognition: From Theory to Applications

✍ Gernot A. Fink 📂 Library 📅 2010 🏛 Springer 🌐 English

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 - cover

From Statistics to Neural Networks: Theo

📁 From Statistics to Neural Networks: Theory and Pattern Recognition Applications

✍ Jerome H. Friedman (auth.), Vladimir Cherkassky, Jerome H. Friedman, Harry Wechs 📂 Library 📅 1994 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

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