In this paper we describe adaptive model selection methods for an extension of the IOHMM called SimIOHMM. We show how to select the initial number of states of the HMM, how to decide when to add new states during the Baum-Welch iterations, and how to modify the Baum-Welch algorithm to efficiently add new nodes. We show that the SimIOHMM with dynamic model selection yields substantial computational gains over the IOHMM with no or little impact on predicting abilities.
By: Vittorio Castelli; Daniel A. Oblinger; Lawrence Bergman; Tessa A. Lau
Published in: RC23395 in 2004
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