A neural model for multi-expert architectures
| dc.creator | Toussaint, Marc | |
| dc.date | 2002-02-19 | |
| dc.date.accessioned | 2026-07-07T05:33:55Z | |
| dc.date.available | 2026-07-07T05:33:55Z | |
| dc.description | We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives. | |
| dc.description | LaTeX, 8 pages, 5 figures | |
| dc.identifier | https://arxiv.org/abs/nlin/0202039 | |
| dc.identifier | http://arxiv.org/abs/nlin/0202039 | |
| dc.identifier | Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), 2755-2760. | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/80173 | |
| dc.subject | Adaptation and Self-Organizing Systems | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.title | A neural model for multi-expert architectures | |
| dc.type | text |