On-line learning and generalization in coupled perceptrons
| Autor: | Désiré Bollé, P. Kozłowski |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Computer Science::Machine Learning
Generalization Computer science business.industry Supervised learning FOS: Physical sciences Physics::Physics Education General Physics and Astronomy Statistical and Nonlinear Physics Disordered Systems and Neural Networks (cond-mat.dis-nn) Condensed Matter - Disordered Systems and Neural Networks Perceptron Condensed Matter::Disordered Systems and Neural Networks Generalization error Term (time) Simple (abstract algebra) Learning curve Line (geometry) Artificial intelligence business Mathematical Physics |
| Zdroj: | Journal of Physics A: Mathematical and General. 35:2093-2109 |
| ISSN: | 1361-6447 0305-4470 |
| DOI: | 10.1088/0305-4470/35/9/302 |
| Popis: | We study supervised learning and generalisation in coupled perceptrons trained on-line using two learning scenarios. In the first scenario the teacher and the student are independent networks and both are represented by an Ashkin-Teller perceptron. In the second scenario the student and the teacher are simple perceptrons but are coupled by an Ashkin-Teller type four-neuron interaction term. Expressions for the generalisation error and the learning curves are derived for various learning algorithms. The analytic results find excellent confirmation in numerical simulations. Latex, 21 pages, 9 figures, iop style files included |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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