Generalization Error Estimation for Non-linear Learning Methods(Neural Networks and Bioengineering)
スポンサーリンク
概要
- 論文の詳細を見る
Estimating the generalization error is one of the key ingredients of supervised learning since a good generalization error estimator can be used for model selection. An unbiased generalization error estimator called the subspace information criterion (SIC) is shown to be useful for model selection, but its range of application is limited to linear learning methods. In this paper, we extend SIC to be applicable to non-linear learning.
- 一般社団法人電子情報通信学会の論文
- 2007-07-01
著者
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Sugiyama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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