Abstract
We present a non-overlapping serial Episodes Based Hierarchical Hidden Markov Model (EBHHMM). Each EBHHMM is associated with two types of temporal frequent patterns, i.e. non-overlapping serial episodes and the presented SEI (Serial Episode Interactions). Serial episode interaction is a set of non-overlapping serial episodes which are correlated and occurs frequently in sequence. As the key advantage of our approach, we do not need any prior-knowledge to learn the structure of EBHHMM. Extensive experiments perform on real world data demonstrate that EBHHMM gets larger maximum log likelihood (has better quality) than existing models.
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Wan, L. (2011). Learning Frequent Episodes Based Hierarchical Hidden Markov Models in Sequence Data. In: Shen, G., Huang, X. (eds) Advanced Research on Computer Science and Information Engineering. CSIE 2011. Communications in Computer and Information Science, vol 153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21411-0_19
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DOI: https://doi.org/10.1007/978-3-642-21411-0_19
Publisher Name: Springer, Berlin, Heidelberg
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