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Design & Implementation of the Parallel-distributed Neural Network Ensemble

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Yue Liu

    (Shanghai University, School of Computer Engineering & Science)

  • Yuan Li

    (Shanghai University, School of Computer Engineering & Science)

  • Bofeng Zhang

    (Shanghai University, School of Computer Engineering & Science)

  • Gengfeng Wu

    (Shanghai University, School of Computer Engineering & Science)

Abstract

Neural network ensemble is a recently developed technology, which trains a few of neural networks and then combines their prediction results. It significantly improves the generalization ability of neural network system and relieves the trial-by-error process of tuning architectures. However, it is time-consuming. In order to overcome the disadvantage, a parallel-distributed neural network ensemble named PDNNE is proposed in this paper. The design and implementation of the PDNNE are presented through discussing the main issues such as partitioning, communication, and the component neural network. The experiments show both the generalization ability and time efficiency are significantly improved.

Suggested Citation

  • Yue Liu & Yuan Li & Bofeng Zhang & Gengfeng Wu, 2005. "Design & Implementation of the Parallel-distributed Neural Network Ensemble," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 103-112, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_10
    DOI: 10.1007/3-540-27912-1_10
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