| Автор | Hyoungsoo Yoon |
| Автор | Jong-Hoon Oh |
| Дата выпуска | 1998-09-25 |
| dc.description | We study learning from examples by higher-order perceptrons, which realize polynomially separable rules. The model complexities of the networks are made `tunable' by varying the relative orders of different monomial terms. We analyse the learning curves of higher-order perceptrons when the Gibbs algorithm is used for training. It is found that learning occurs in a stepwise manner. This is because the number of examples needed to constrain the corresponding phase-space component scales differently. |
| Формат | application.pdf |
| Издатель | Institute of Physics Publishing |
| Название | Learning of higher-order perceptrons with tunable complexities |
| Тип | paper |
| DOI | 10.1088/0305-4470/31/38/012 |
| Print ISSN | 0305-4470 |
| Журнал | Journal of Physics A: Mathematical and General |
| Том | 31 |
| Первая страница | 7771 |
| Последняя страница | 7784 |
| Аффилиация | Hyoungsoo Yoon; Physics Department and Basic Science Research Institute, Pohang University of Science and Technology, Pohang, South Korea 790-784 |
| Аффилиация | Jong-Hoon Oh; Physics Department and Basic Science Research Institute, Pohang University of Science and Technology, Pohang, South Korea 790-784 |
| Выпуск | 38 |
