A New Meta-Criterion for Regularized Subspace Information Criterion

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概要

• 論文の詳細を見る

• In order to gain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the generalization error is minimized. However, since the generalization error is inaccessible in practice, the model parameters are usually determined so that an estimator of the generalization error is minimized. The regularized subspace information criterion (RSIC) is such a generalization error estimator for model selection. RSIC includes a regularization parameter and it should be determined appropriately for better model selection. A meta-criterion for determining the regularization parameter has also been proposed and shown to be useful in practice. In this paper, we show that there are several drawbacks in the existing meta-criterion and give an alternative meta-criterion that can solve the problems. Through simulations, we show that the use of the new meta-criterion further improves the model selection performance.

• 社団法人電子情報通信学会の論文
• 2007-01-18

著者

• Sugiyama Masashi

  Tokyo Inst. Of Technol. Tokyo Jpn

• SUGIYAMA Masashi

  Department of Computer Science, Tokyo Institute of Technology

• HIDAKA Yasushi

  Department of Computer Science, Tokyo Institute of Technology

• Hidaka Yasushi

  Department Of Computer Science Tokyo Institute Of Technology

• Sugiyama Masashi

  Department Of Computer Science Tokyo Institute Of Technology

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

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