Hidden Semi-Markov models

Hidden Semi-Markov models: theory, algorithms and applications

by Yu, Shun-Zheng

Type Book
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Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algor…

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Title Statements

  • Full title :Hidden Semi-Markov models : theory, algorithms and applications / Shun-Zheng Yu [aut]

Summary

Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.

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Edition Statements

  • Publication Date:2016

Publication Statements

  • Publisher(s):Elsevier
  • Place of Publication:Amsterdam, Netherlands
  • Language:eng
  • Publication Date:2016

The Physical Object

  • Description:1 online resource

Edition Identifiers

  • ISBN:9780128027714, 0128027711, 0128027673, 9780128027677
  • oclc:on1281802534

Writers

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