Name
Abstract | ||
|---|---|---|
Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of sufficient complexity to solve a problem it is trained on. GAM's representation is a gaussian mixture model of the input space, with learned mappings from the mixture components to ou... |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1162/neco.1997.9.7.1517 | Neural Computation |
Keywords | DocType | Volume |
incremental-learning network,mixture modeling,gaussian mixture model,supervised learning,mixture model,expectation maximization,receptive field,adaptive resonance theory | Journal | 9 |
Issue | ISSN | Citations |
7 | 0899-7667 | 16 |
PageRank | References | Authors |
1.19 | 8 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James R. Williamson | 1 | 389 | 31.64 |