Learning DYllamical Systems Using Hidden Markov Models
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چکیده
In this paper, we address the problem of learning models for complex processes under the assumption that the processes can be represented within the hidden Markov model (HMM) framework. Toward that aim, we investigate the strengths and weaknesses of two compet ing algorithms for learning HMMs: Baum-VVelch and Bayesian Model Merging. VVe offer insight into the reasons for the success or failure of each algorithm, especially through em pirical trials, in several domains. Our experiments support the conclusion that Bayesian Model Merging suffers a number of disadvantages which suggest Baum-VVelch be preferred for learning a particular class of HMMs.
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تاریخ انتشار 1996