Abstract
Summary form only given, as follows. Supervised learning with artificial selection is proposed as a way to escape from local minima. The concept of artificial selection is reasonable for nature. In the authors' method, the 'worst' hidden unit is detected, and then all the weights connected to the detected hidden unit are reset to small random values. According to simulations, only half the trials using conventional backpropagation converge, whereas all of the trials using the proposed method converge, and quickly do so.
| Original language | English |
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| Pages | 611 |
| Number of pages | 1 |
| Publication status | Published - 1989 |
| Event | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA Duration: 1989 Jun 18 → 1989 Jun 22 |
Other
| Other | IJCNN International Joint Conference on Neural Networks |
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| City | Washington, DC, USA |
| Period | 89/6/18 → 89/6/22 |
ASJC Scopus subject areas
- General Engineering