| Автор | Magnus Rattray |
| Автор | David Saad |
| Дата выпуска | 1997-11-21 |
| dc.description | We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in comparison. |
| Формат | application.pdf |
| Издатель | Institute of Physics Publishing |
| Название | Globally optimal on-line learning rules for multi-layer neural networks |
| Тип | lett |
| DOI | 10.1088/0305-4470/30/22/005 |
| Print ISSN | 0305-4470 |
| Журнал | Journal of Physics A: Mathematical and General |
| Том | 30 |
| Первая страница | L771 |
| Последняя страница | L776 |
| Аффилиация | Magnus Rattray; Department of Computer Science and Applied Mathematics, Aston University, Birmingham B4 7ET, UK |
| Аффилиация | David Saad; Department of Computer Science and Applied Mathematics, Aston University, Birmingham B4 7ET, UK |
| Выпуск | 22 |
