Abstract
Summary form only given, as follows. Neural networks have traditionally been applied to recognition problems, and most learning algorithms are tailored to those problems. The authors discuss the requirements of learning for generalization, which is NP-complete and cannot be approached by traditional methods based on gradient descent. They present a stochastic learning algorithm based on simulated annealing in weight space. The authors verify the convergence properties and feasibility of the algorithm.
| Original language | English (US) |
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| Title of host publication | IJCNN Int Jt Conf Neural Network |
| Editors | Anon |
| Publisher | Publ by IEEE |
| Number of pages | 1 |
| State | Published - Dec 1 1989 |
| Event | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA Duration: Jun 18 1989 → Jun 22 1989 |
Other
| Other | IJCNN International Joint Conference on Neural Networks |
|---|---|
| City | Washington, DC, USA |
| Period | 6/18/89 → 6/22/89 |