Hidden Semi-Markov models: theory, algorithms and applications

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 d...

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Bibliographische Detailangaben
1. Verfasser: Yu, Shun-Zheng (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Amsterdam, Netherlands Elsevier [2016]
Schriftenreihe:Computer science reviews and trends
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Online-Zugang:FLA01
URL des Erstveröffentlichers
Zusammenfassung: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
Beschreibung:Includes bibliographical references
Beschreibung:1 online resource illustrations
ISBN:9780128027714
0128027711
0128027673
9780128027677

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