Name
Abstract | ||
|---|---|---|
Genetics based machine learning systems are considered by a majority of machine learners as slow rate learning systems. In this paper, we propose an improvement of Wilson's classifier system BOOLE that shows how Genetics based machine learning systems learning rates can be greatly improved. This modification consists in a change of the reinforcement component. We then compare the respective performances of this modified BOOLE, called NEWBOOLE, and a neural net using back propagation on a difficult boolean learning task, the multiplexer function. The results of this comparison show that NEWBOOLE obtains significantly faster learning rates. |
Year | DOI | Venue |
|---|---|---|
1990 | 10.1016/B978-1-55860-141-3.50022-5 | ML |
Keywords | Field | DocType |
fast gbml system | Online machine learning,Semi-supervised learning,Stability (learning theory),Multi-task learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory,Machine learning,Learning classifier system | Conference |
Issue | ISBN | Citations |
1 | 1-55860-141-4 | 38 |
PageRank | References | Authors |
2.65 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre Bonelli | 1 | 113 | 12.39 |
| Alexandre Parodi | 2 | 113 | 12.39 |
| Sandip Sen | 3 | 1695 | 203.66 |
| Stewart Wilson | 4 | 54 | 8.84 |