Name
Abstract | ||
|---|---|---|
Lazy learning methods have been proved useful when dealing with problems in which the learning examples have multiple local functions. These methods are related with the selection, for training purposes, of a subset of examples, ancl making some linear combination to generate the output. On the other hand, neural network are eager learning methods that have a high nonlinear behavior. In this work, a lazy method is proposed for Radial Basis Neural Networks in order to improve both, the generalization capability of those networks for some specific domains, and the performance of classical lazy learning mnethods. A comparison with some lazy mnethods, and RBNN trained as usual is made, and the new approach shows good results in two test domains, a real life problem and an artificial domain. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-44868-3_37 | IWANN (1) |
Keywords | Field | DocType |
radial basis neural networks,lazy mnethods,artificial domain,lazy method,generalization capability,eager learning method,lazy learning method,high nonlinear behavior,iterative fuzzy prototype induction,good result,classical lazy learning mnethods | Linear combination,Instance-based learning,Computer science,Iterative method,Fuzzy logic,Eager learning,Lazy learning,Algorithm,Fuzzy set,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
2686 | 0302-9743 | 3-540-40210-1 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Inés González Rodríguez | 1 | 72 | 6.52 |
| Jonathan Lawry | 2 | 172 | 19.06 |
| Jim F. Baldwin | 3 | 17 | 1.70 |