Abstract
In modern society, the more complex information and technology become, the more important data analysis become. In particular, a data, which has a variety of elements, is complex, and it is extremely difficult to estimate the state which generates data from observed data. To handle those hidden states, we propose an appropriate model using Spherical-Self Organizing Map (S-SOM) with Hidden Markov Model (HMM) which can estimate the hidden state.
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Niina, G., Dozono, H. (2012). The Spherical Hidden Markov Self Organizing Map for Learning Time Series Data. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_71
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DOI: https://doi.org/10.1007/978-3-642-33269-2_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
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