Abstract
Many learning algorithms tend to converge into local minima that often represent partial solutions. Schemes are presented that greatly minimize the risk of converging to a partial solution and maximize the rule discovery process for rule-based learning. For the experiments, a genetic algorithm rule-based learning system called a classifier system has been used. The new strategies are supported by presenting accelerations and completion of learning in higher order letter image classification problems.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Publisher | IEEE Computer Society |
| Pages | 1393-1398 |
| Number of pages | 6 |
| Volume | 2 |
| State | Published - 1991 |
| Externally published | Yes |
| Event | Conference Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics - Charlottesville, VA, USA Duration: Oct 13 1991 → Oct 16 1991 |
Other
| Other | Conference Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics |
|---|---|
| City | Charlottesville, VA, USA |
| Period | 10/13/91 → 10/16/91 |
ASJC Scopus subject areas
- Hardware and Architecture
- Control and Systems Engineering