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First Order Hidden Markov Model: Theory and Implementation Issues
Blekinge Institute of Technology, School of Engineering, Department of Signal Processing.
2005 (English)Report (Refereed)Alternative title
Första Ordningens Gömda Markov Kedjor : Teori och Implementerings Aspekter (Swedish)
Abstract [en]

This report explains the theory of Hidden Markov Models (HMMs). The emphasis is on the theory aspects in conjunction with the implementation issues that are encountered in a floating point processor. The main theory and implementation issues are based on the use of a Gaussian Mixture Model (GMM) as the state density in the HMM, and a Continuous Density Hidden Markov Model (CDHMM) is assumed. Suggestions and advice related to the implementation are given for a typical pattern recognition task.

Place, publisher, year, edition, pages
Blekinge Tekniska Högskola Forskningsrapport, ISSN 1103-1581 ; 2
Keyword [en]
HMM, GMM, SOFM, k-means, Baum-Welch, Viterbi, Pattern Recognition
National Category
Signal Processing Probability Theory and Statistics
URN: urn:nbn:se:bth-00271Local ID: diva2:833697
Available from: 2015-06-25 Created: 2005-02-28 Last updated: 2015-06-30Bibliographically approved

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Nilsson, Mikael
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