Tutorial on Hidden Markov Model
Applied and Computational Mathematics
Volume 6, Issue 4-1, July 2017, Pages: 16-38
Received: Sep. 11, 2015; Accepted: Sep. 13, 2015; Published: Jun. 17, 2016
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Author
Loc Nguyen, Sunflower Soft Company, Ho Chi Minh city, Vietnam
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Abstract
Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on HMM with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on three common problems of HMM such as evaluation problem, uncovering problem, and learning problem, in which learning problem with support of optimization theory is the main subject.
Keywords
Hidden Markov Model, Optimization, Evaluation Problem, Uncovering Problem, Learning Problem
To cite this article
Loc Nguyen, Tutorial on Hidden Markov Model, Applied and Computational Mathematics. Special Issue:Some Novel Algorithms for Global Optimization and Relevant Subjects. Vol. 6, No. 4-1, 2017, pp. 16-38. doi: 10.11648/j.acm.s.2017060401.12
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