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Learning Mixture Models for Classification with Energy Combination

Author

Listed:
  • Chi-Ming Tsou

    (Lunghwa University of Science and Technology, Taiwan)

  • Chuan Chen

    (Fu-Jen Catholic University, Taiwan)

  • Deng-Yuan Huang

    (Fu-Jen Catholic University, Taiwan)

Abstract

In this article, we propose a technique called Energy Mixture Model (EMM) for classification. EMM is a type of feed-forward neural network that can be used to decide the number of nodes for constructing the hidden layer of neural networks based on the variable clustering method. Additionally, energy combination method is used to generate the recognition pattern as the basis for classification. This approach not only improves the elucidation capability of the model but also discloses the black box of the hidden layer of neural networks. Domain experts can evaluate models built by variable clusters more easily than those built by neural networks.

Suggested Citation

  • Chi-Ming Tsou & Chuan Chen & Deng-Yuan Huang, 2007. "Learning Mixture Models for Classification with Energy Combination," Management, University of Primorska, Faculty of Management Koper, vol. 2(3), pages 203-214.
    • Handle: RePEc:mgt:youmng:v:2:y:2007:i:3:p:203-214
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    References listed on IDEAS

    as
    1. Vermunt, Jeroen K. & Magidson, Jay, 2003. "Latent class models for classification," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 531-537, January.
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