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Hidden semi-Markov modelstheory, alg...

Yu, Shun-Zheng.

Hidden semi-Markov modelstheory, algorithms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hidden semi-Markov modelsShun-Zheng Yu.
Reminder of title:	theory, algorithms and applications /
Author:	Yu, Shun-Zheng.
Published:	Amsterdam, Netherlands :Elsevier,2016.
Description:	1 online resource :ill.
Subject:	Markov processes.
Online resource:	https://www.sciencedirect.com/science/book/9780128027677
ISBN:	9780128027714 (electronic bk.)

Hidden semi-Markov modelstheory, algorithms and applications /
Yu, Shun-Zheng.

Hidden semi-Markov modelstheory, algorithms and applications /[electronic resource] :Shun-Zheng Yu. - Amsterdam, Netherlands :Elsevier,2016. - 1 online resource :ill. - Computer science reviews and trends.

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.

ISBN: 9780128027714 (electronic bk.)Subjects--Topical Terms:

181910
Markov processes.

LC Class. No.: QA274.7

Dewey Class. No.: 519.2/33

Hidden semi-Markov modelstheory, algorithms and applications /
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520 $a 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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