A supervised learning algorithm for hierarchical classification of fuzzy patterns
β Scribed by Prasenjit Biswas; Arun K. Majumdar
- Publisher
- Elsevier Science
- Year
- 1983
- Tongue
- English
- Weight
- 807 KB
- Volume
- 31
- Category
- Article
- ISSN
- 0020-0255
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
This paper presents a novel boosting algorithm for genetic learning of fuzzy classiΓΏcation rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one
Reinforcement learning has been widely-used for applications in planning, control, and decision making. Rather than using instructive feedback as in supervised learning, reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern class
Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various varia