Realization of Admissibility for Supervised Learning

スポンサーリンク

概要

• 論文の詳細を見る

• In supervised learning, one of the major learning methods is memorization learning(ML). Since it reduces only the training error, ML does not guarantee good generalization capability in general. When ML is used, however, acquiring good generalization capability is expected. This usage of ML was interpreted by one of the present authors, H.Ogawa, as a means of realizing 'true objective learning' which directly takes generalization capability into account, and introduced the concept of admissibility. If a learning method can provide the same generalization capability as a true objective learning, it is said that the objective learning admits the learning method. Hence, if admissibility does not hold, making it hold becomes important. In this paper, we introduce the concept of realization of admissibility, and devise a realization method of admissibility of ML with respect to projection learning which directly takes generalization capability into account.

• 社団法人電子情報通信学会の論文
• 2000-05-25

著者

• OGAWA Hidemitsu

  The authors are with Department of Computer Science, Tokyo Institute of Technology

• Nakashima Akiko

  The Author Is With The Research & Development Center Toshiba Corporation

• HIRABAYASHI Akira

  The authors are with the Graduate School of Information Science and Engineering, Tokyo Institute of

• Hirabayashi Akira

  The Authors Are With The Graduate School Of Information Science And Engineering Tokyo Institute Of T

• Ogawa Hidemitsu

  The Authors Are With The Graduate School Of Information Science And Engineering Tokyo Institute Of T

関連論文

• Active Learning with Model Selection : Simultaneous Optimization of Sample Points and Models for Trigonometric Polynomial Models(Pattern Recognition)
• Realization of Admissibility for Supervised Learning

スポンサーリンク