Affiliations: Department of Computer Science, Middle Tennessee State
University, Box 48, Murfreesboro, TN 37132, Tel.: +1 615 904 8168; Fax: +1 615
898 5567; E-mail: [email protected] | Department of Electrical and Computer Engineering,
Vanderbilt University, Box 1679 Station B, Nashville, TN 37235, USA. Tel.: +1
615 343 6204; Fax: +1 615 343 5459; E-mail: [email protected]
Abstract: Hidden Markov Models(HMM) have proved to be a successful modeling
paradigm for dynamic and spatial processes in many domains, such as speech
recognition, genomics, and general sequence alignment. Typically, in these
applications, the model structures are predefined by domain experts. Therefore,
the HMM learning problem focuses on the learning of the parameter values of the
model to fit the given data sequences. However, when one considers other
domains, such as, economics and physiology, model structure capturing the
system dynamic behavior is not available. In order to successfully apply the
HMM methodology in these domains, it is important that a mechanism is available
for automatically deriving the model structure from the data. This paper
presents a HMM learning procedure that simultaneously learns the model
structure and the maximum likelihood parameter values of a HMM from data. The
HMM model structures are derived based on the Bayesian model selection
methodology. In addition, we introduce a new initialization procedure for HMM
parameter value estimation based on the K-means clustering method. Experimental
results with artificially generated data show the effectiveness of the
approach.
