Abstract
Hierarchical hidden Markov models (HHMMs) can be used for time series segmentation. However, it is difficult to obtain a desirable segmentation result, because the form of learning for HHMMs is unsupervised. In the paper, we present a semisupervised learning algorithm for HHMMs. It is semisupervised in the sense that the supervisor teaches segmentation boundaries but not segment labels. The learning performance of the proposed algorithm is demonstrated through an experiment using music data.
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© 2005 Springer-Verlag Berlin Heidelberg
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Gotou, N., Hayashi, A., Suematu, N. (2005). Learning with Segment Boundaries for Hierarchical HMMs. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_59
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DOI: https://doi.org/10.1007/11551188_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
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