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Hidden Semi-Markov Models
Theory, Algorithms and Applications
The latest information, new developments and emerging topics about HSMMs, including illustrated examples, with a more in-depth treatment and foundational approach in the understanding and application of HSMMs.
Shun-Zheng Yu (Author)
9780128027677, Elsevier Science
Paperback, published 27 October 2015
208 pages
22.9 x 15.1 x 1.4 cm, 0.39 kg
"This book is intended to present theory, models, methods, and applications regarding hidden semi-Markov models...It also provides the latest development and emerging topics concerning this field." --Zentralblatt MATH
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.
1. Introduction2. Inference of General Hidden Semi-Markov Model3. Estimation of General Hidden Semi-Markov Model4. Implementation of the Algorithms5. Conventional Models6. Various Duration Distributions8. Variants of HSMM9. Applications of HSMM
Subject Areas: Machine learning [UYQM], Expert systems / knowledge-based systems [UYQE], Artificial intelligence [UYQ], Probability & statistics [PBT]
