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Subnet Weight Modification Algorithm for Ensemble

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Intelligent Computing (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4113))

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Abstract

In view of comparability between neural network ensemble and Adaline, a modification algorithm for ensemble weights is presented based on the gradient descent method. This algorithm can improve the generalization performance by modifying subnet weights after the ensemble subnets are trained individually. Simulation results indicate that the new algorithm is of subnet selection function similar to GASEN but on a different idea, and of better performance than single network, simple-averaged ensemble and GASEN.

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Author information

Authors and Affiliations

  1. College of Mechanical Engineering and Automatization, North University of China, Taiyuan, 030051, Shanxi, China

    Jiang Meng

  2. College of Information and Communication Engineering, North University of China, Taiyuan, 030051, Shanxi, China

    Kun An

  3. Shanghai Institute of Technology, School of Mechanical and Automation Engineering, Shanghai, 200235, China

    Zhijie Wang

Authors
  1. Jiang Meng
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  2. Kun An
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  3. Zhijie Wang
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Carnegie Mellon University,  

    Kang Li

  3. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Stranmillis Road, BT9 5AH, Belfast, UK

    George William Irwin

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© 2006 Springer-Verlag Berlin Heidelberg

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Meng, J., An, K., Wang, Z. (2006). Subnet Weight Modification Algorithm for Ensemble. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_60

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  • DOI: https://doi.org/10.1007/11816157_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37271-4

  • Online ISBN: 978-3-540-37273-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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