Abstract
Input/output hidden Markov model (IOHMM) has turned out to be effective in sequential data processing via supervised learning. However, there are several difficulties, e.g. model selection, unexpected local optima and high computational complexity, which hinder an IOHMM from yielding the satisfactory performance in sequence classification. Unlike previous efforts, this paper presents an ensemble learning approach to tackle the aforementioned problems of the IOHMM. As a result, simple IOHMMs of different topological structures are used as base learners in our boosting algorithm and thus an ensemble of simple IOHMMs tend to tackle a complicated sequence classification problem without the need of explicit model selection. Simulation results in text-dependent speaker identification demonstrate the effectiveness of boosted IOHMMs for sequence classification.
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Chen, K. (2005). Boosting Input/Output Hidden Markov Models for Sequence Classification. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_94
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DOI: https://doi.org/10.1007/11539117_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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