Abstract
Generalization ability of neural networks is the most important criterion to determine whether one algorithm is powerful or not. Many new algorithms have been devised to enhance the generalization ability of neural networks[1][2]. In this paper a new algorithm using the Gram-Schmidt orthogonalization algorithm [3] to the outputs of nodes in the hidden layers is proposed with the aim to reduce the interference among the nodes in the hidden layers, which is much more efficient than the regularizers methods. Simulation results confirm the above assertion.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Pages | 1721-1726 |
| Number of pages | 6 |
| Volume | 3 |
| Publication status | Published - 2001 |
| Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: Jul 15 2001 → Jul 19 2001 |
Other
| Other | International Joint Conference on Neural Networks (IJCNN'01) |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 7/15/01 → 7/19/01 |
All Science Journal Classification (ASJC) codes
- Software
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Wan, W., Hirasawa, K., Hu, J., & Murata, J. (2001). Enhancing the generalization ability of neural networks by using gram-schmidt orthogonalization algorithm. In Proceedings of the International Joint Conference on Neural Networks (Vol. 3, pp. 1721-1726)