Adaptive Ridge Learning in Kernel Eigenspace and Its Model Selection

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概要

• 論文の詳細を見る

• In order to obtain better learning results in supervised learning, it is important to choose model parameters appropriately. Model selection is usually carried out by preparing a finite set of model candidates, estimating a generalization error for each candidate, and choosing the best one from the candidates. If the number of candidates is increased in this procedure, the optimization quality may be improved. However, this in turn increases the computational cost. In this paper, we focus on a generalization error estimator called the regularized subspace information criterion and derive an analytic form of the optimal model parameter over a set of infinitely many model candidates. This allows us to maximize the optimization quality with the computational cost kept moderate.

• 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

• GOKITA Shun

  Department of Computer Science, Tokyo Institute of Technology

• SAKURAI Keisuke

  Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology

• Gokita Shun

  Department Of Computer Science Tokyo Institute Of Technology

• Sakurai Keisuke

  Department Of Computational Intelligence And Systems Science Tokyo Institute Of Technology

• Sakurai Keisuke

  Department Of Biophysics Graduate School Of Science Kyoto University:core Reserch For Evolutional Sc

• 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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