Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion(Neural Networks and Bioengineering)

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概要

• 論文の詳細を見る

• For obtaining a higher level of generalization capability in supervised learning, model parameters should be optimized, i.e., they should be determined in such a way that the generalization error is minimized. However, since the generalization error is inaccessible in practice, model parameters are usually determined in such a way that an estimate of the generalization error is minimized. A standard procedure for model parameter optimization is to first prepare a finite set of candidates of model parameter values, estimate the generalization error for each candidate, and then choose 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 give methods for analytically finding the optimal model parameter value from a set of infinitely many candidates. This maximally enhances the optimization quality while the computational cost is kept reasonable.

• 社団法人電子情報通信学会の論文
• 2006-08-01

著者

• SUGIYAMA Masashi

  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 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

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

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