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Mixture of Experts for Stellar Data Classification

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

In this paper, mixture of experts model is first applied to stellar data classification. In order to obtain input patterns of mixture of experts model, we present a feature extraction method for stellar data based on wavelet packet transformation. Then a mixture of experts model is built for classifying the feature vectors. A comparative study of different classification methods such as a single radial basis function neural network is given. Real world data experimental results show that the mixture of experts has a good generalization ability and the obtained correct classification rate is higher than that of using a single neural network.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Beijing Normal University, Beijing, 100875, China

    Yugang Jiang & Ping Guo

Authors
  1. Yugang Jiang
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  2. Ping Guo
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Jiang, Y., Guo, P. (2005). Mixture of Experts for Stellar Data Classification. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_50

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  • DOI: https://doi.org/10.1007/11427445_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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