Supervised learning with artificial selection

Masafumi Hagiwara, Masao Nakagawa

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

Summary form only given, as follows. Supervised learning with artificial selection is proposed as a way to escape from local minima. The concept of artificial selection is reasonable for nature. In the authors' method, the 'worst' hidden unit is detected, and then all the weights connected to the detected hidden unit are reset to small random values. According to simulations, only half the trials using conventional backpropagation converge, whereas all of the trials using the proposed method converge, and quickly do so.

Original languageEnglish
Number of pages1
Publication statusPublished - 1989 Dec 1
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: 1989 Jun 181989 Jun 22

Other

OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA
Period89/6/1889/6/22

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Hagiwara, M., & Nakagawa, M. (1989). Supervised learning with artificial selection. Paper presented at IJCNN International Joint Conference on Neural Networks, Washington, DC, USA, .