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月 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月 27 → 1994 6月 29 |
Other
| Other | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
|---|---|
| City | Orlando, FL, USA |
| Period | 94/6/27 → 94/6/29 |
ASJC Scopus subject areas
- ソフトウェア