Self-Organizing Hidden Markov Models

Author:

Yamaguchi Nobuhiko

Publisher

Springer Berlin Heidelberg

Reference7 articles.

1. Edward, G., Box, P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Prentice Hall PTR, Upper Saddle River (1994)

2. Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (1994)

3. Verbeek, J., Vlassis, N., Kröse, B.: Self-organizing mixture models. Neurocomputing 63, 99–123 (2005)

4. Verbeek, J.: Mixture Models for Clustering and Dimension Reduction. PhD thesis, University of Amsterdam (2004)

5. McLachlan, G.J., Peel, D.: Finite Mixture Models. John Wiley & Sons, Inc., New York (2000)

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1. Hidden Markov models on a self-organizing map for anomaly detection in 802.11 wireless networks;Neural Computing and Applications;2021-01-02

2. Probabilistic Self-Organizing Map for Clustering and Visualizing non-i.i.d Data;International Journal of Computational Intelligence and Applications;2015-06

3. Basic Study on the Classification of Time Series Data Using a Frequency Integrated Spherical Hidden Markov Self Organizing Map;Journal of Advanced Computational Intelligence and Intelligent Informatics;2015-03-20

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