Abstract
We propose in this paper a novel approach which makes self-organizing maps (SOM) and the Hidden Markov Models (HMMs) cooperate. Our approach (SOS-HMM: Self Organizing Structure of HMM) allows to learn the Hidden Markov Models topology. The main contribution for the proposed approach is to automatically extract the structure of a hidden Markov model without any prior knowledge of the application domain. This model can be represented as a graph of macro-states, where each state represents a micro model. Experimental results illustrate the advantages of the proposed approach compared to a fixed structure approach.
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Jaziri, R., Lebbah, M., Bennani, Y., Chenot, JH. (2011). SOS-HMM: Self-Organizing Structure of Hidden Markov Model. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_12
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DOI: https://doi.org/10.1007/978-3-642-21738-8_12
Publisher Name: Springer, Berlin, Heidelberg
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