Prediction error of stochastic learning machine

Kazushi Ikeda, Noboru Murata, Shun Ichi Amari

研究成果: Paper査読

抄録

The more the number of training examples increases, the better a learning machine will behave. It is an important problem to know how fast and how well the behavior is improved. The average prediction error is one of the most popular criteria to evaluate the behavior. We have regarded the machine learning from the point of view of parameter estimation and derived the average prediction error of stochastic dichotomy machines by the information geometrical method.

本文言語English
ページ1159-1162
ページ数4
出版ステータスPublished - 1994 12 1
外部発表はい
イベントProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
継続期間: 1994 6 271994 6 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period94/6/2794/6/29

ASJC Scopus subject areas

  • Software

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