Abstract
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.
| Original language | English |
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| Pages | 1159-1162 |
| Number of pages | 4 |
| Publication status | Published - 1994 |
| Externally published | Yes |
| Event | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: 1994 Jun 27 → 1994 Jun 29 |
Other
| Other | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
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| City | Orlando, FL, USA |
| Period | 94/6/27 → 94/6/29 |
ASJC Scopus subject areas
- Software