Abstract
In the search for even better parsimonious neural network modeling, this paper describes a novel approach which attempts to exploit redundancy found in the conventional sigmoidal networks. A hybrid universal learning network constructed by the combination of proposed multiplication units with summation units is trained for several classification problems. It is clarified that the multiplication units in different layers in the network improve the performance of the network.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Pages | 703-708 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 2002 |
| Event | 2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002 |
Other
| Other | 2002 International Joint Conference on Neural Networks (IJCNN '02) |
|---|---|
| Country | United States |
| City | Honolulu, HI |
| Period | 5/12/02 → 5/17/02 |
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All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
Cite this
Li, D., Hirasawa, K., Hu, J., & Murata, J. (2002). Training a kind of hybrid universal learning networks with classification problems. In Proceedings of the International Joint Conference on Neural Networks (Vol. 1, pp. 703-708)