Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion

• GOKITA Shun

  Department of Computer Science, Tokyo Institute of Technology

• SUGIYAMA Masashi

  Department of Computer Science, Tokyo Institute of Technology

• SAKURAI Keisuke

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

Abstract

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 while the computational cost is kept moderate.

Journal

• IEICE transactions on fundamentals of electronics, communications and computer sciences

  IEICE transactions on fundamentals of electronics, communications and computer sciences 90 (11), 2584-2592, 2007-11-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• supervised learning
• generalization error
• model selection
• regularized subspace information criterion

Details

• CRID: 1573950402335474944
• NII Article ID: 110007537996
• NII Book ID: AA10826239
• ISSN: 09168508
• Text Lang: en
• Data Source:
  • CiNii Articles