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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
2005.
Series
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
Identifiers
URN: urn:nbn:se:bth-00271Local ID: oai:bth.se:forskinfo36147C58EC7998A0C1256FB600482AC2OAI: oai:DiVA.org:bth-00271DiVA: diva2:833697
Available from: 2015-06-25 Created: 2005-02-28 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(1466 kB)348 downloads
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Type fulltextMimetype application/pdf

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Nilsson, Mikael
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf