書誌事項
- タイトル別名
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- A Study for Keeping Generalization Ability of Multilayered Neural Networks using Evolution Strategies
- シンカ センリャク ノ テキヨウ ニヨル カイソウガタ ネットワーク ノ ハン
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This paper proposes a new algorithm for advancing the generalization ability of multilayer neural networks. The proposed algorithm, based on regularization theory, is a method for determining the regularization parameter, on condition that the training data is shown additionally. It is not a method that solves a problem for all training data again when additional training data is shown, but rather a method that adjusts the regularization parameter to fit additional training data. The characteristics of this algorithm are (1) the prediction error for the additional data is used in evaluating to determine the regularization parameter, (2) Evolution strategies (ES) that is multipoint search method is used for the determination problem of regularization parameter. The evaluation of the regularization parameter varies according to the data added. This study simulated an additional learning problem to examine the performance of the proposed method. And the simulation results are presented in this paper.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 117 (2), 143-149, 1997
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679584426496
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- NII論文ID
- 130006843615
- 10002809323
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 4128398
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可