Ensemble of competitive associative nets and a method to select an effective number of units

  • Authors:

  • Shuichi Kurogi;Shinya Tanaka;Ryohei Koyama;Takeshi Nishida

  • Affiliations:

  • Kyushu Institute of Technology, Fukuoka, Japan;Kyushu Institute of Technology, Fukuoka, Japan;Kyushu Institute of Technology, Fukuoka, Japan;Kyushu Institute of Technology, Fukuoka, Japan

  • Venue:
  • ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
  • Year:
  • 2006

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Abstract

The competitive associative net called CAN2 has been shown effective in many applications, such as function approximation, control, rainfall estimation, time-series prediction, and so on, but the learning method has been constructed basically for reducing the training (empirical) error. In order to reduce prediction (generalization) error, we, in this article, try to apply the ensemble scheme to the CAN2 and present a method to select an effective number of units for the ensemble. We show the result of numerical experiments and examine the effectiveness of the present method.