Abstract
Structured Hidden Markov Model (S-HMM) is a variant of Hierarchical Hidden Markov Model that shows interesting capabilities of extracting knowledge from symbolic sequences. In fact, the S-HMM structure provides an abstraction mechanism allowing a high level symbolic description of the knowledge embedded in S-HMM to be easily obtained. The paper provides a theoretical analysis of the complexity of the matching and training algorithms on S-HMMs. More specifically, it is shown that the Baum-Welch algorithm benefits from the so called locality property, which allows specific components to be modified and retrained, without doing so for the full model. Moreover, a variant of the Baum-Welch algorithm is proposed, which allows a model to be biased towards specific regularities in the training sequences, an interesting feature in a knowledge extraction task. Several methods for incrementally constructing complex S-HMMs are also discussed, and examples of application to non trivial tasks of profiling are presented.
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Galassi, U., Giordana, A., Saitta, L. (2008). Structured Hidden Markov Models: A General Tool for Modeling Agent Behaviors. In: Prasad, B. (eds) Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79005-1_15
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DOI: https://doi.org/10.1007/978-3-540-79005-1_15
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