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.
|出版ステータス||Published - 1994 12 1|
|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 27 → 1994 6 29
|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|
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