Statistical Mechanics of On-Line Learning Using Correlated Examples

スポンサーリンク

概要

• 論文の詳細を見る

• We consider a model composed of nonlinear perceptrons and analytically investigate its generalization performance using correlated examples in the framework of on-line learning by a statistical mechanical method. In Hebbian and AdaTron learning, the larger the number of examples used in an update, the slower the learning. In contrast, Perceptron learning does not exhibit such behaviors, and the learning becomes fast in some time region.

• （社）電子情報通信学会の論文
• 2011-10-01

著者

• Miyoshi Seiji

  The Faculty Of Engineering Science Kansai University

• NAKAO Kento

  the Graduate School of Science and Engineering, Kansai University

• NARUKAWA Yuta

  DAIHEN Corporation

• Nakao Kento

  The Graduate School Of Science And Engineering Kansai University

関連論文

• Statistical Mechanics of On-Line Learning Using Correlated Examples
• Statistical Mechanics of Adaptive Weight Perturbation Learning

スポンサーリンク