Abstract
Learning algorithms guided by costs with a variety of penalties are discussed. Both unsupervised and supervised cases are addressed. The penalties are added and/or multiplied to the basic error measure. Since these extra penalties include combination parameters with respect to the basic error, the total problem belongs to a class of multiple object optimization. Learning algorithms on general cases are derived first. Then, individual cases such as penalties on undesirable weights and outputs are treated. A method to find a preferred solution among the Pareto optimal set of the multiple object optimization is given.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | IEEE |
| Pages | 187-192 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 1994 |
| Externally published | Yes |
| Event | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: 1994 Jun 27 → 1994 Jun 29 |
Other
| Other | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
|---|---|
| City | Orlando, FL, USA |
| Period | 94/6/27 → 94/6/29 |
ASJC Scopus subject areas
- Software