Adopting Relational Reinforcement Learning in Covering Algorithms for Numeric and Noisy Environments
Covering algorithms (CAs) constitute a type of inductive learning for the discovery of simple rules to predict future activities. Although this approach produces powerful models for datasets with discrete features, its applicability to problems involving noisy or numeric (continuous) features has be...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Atlantis Press
2016-06-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868712/view |