New random search method for neural network learning - RasID

Jinglu Hu*, Kotaro Hirasawa, Junichi Mutata, Masanao Ohbayashi, Yurio Eki

*この研究の対応する著者

研究成果: Paper査読

2 被引用数 (Scopus)

抄録

This paper presents a novel random searching scheme called RasID for neural networks training. The idea is to introduce a sophisticated probability density function (PDF) for generating search vector. The PDF provides two parameters for realizing intensified search in the area where it is likely to find good solutions locally or diversified search in order to escape from a local minimum based on the success-failure of the past search. Gradient information is used to improve the search performance. The proposed scheme is applied to layered neural networks training and is benchmarked against other deterministic and non-deterministic methods.

本文言語English
ページ2346-2351
ページ数6
出版ステータスPublished - 1998 1月 1
外部発表はい
イベントProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
継続期間: 1998 5月 41998 5月 9

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period98/5/498/5/9

ASJC Scopus subject areas

  • ソフトウェア

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