Abstract
Summary form only given, as follows. A class of neural-network architectures is described that uses both distributed and local representation. The distributed representations are used for input and output, thereby enabling associative, noise-tolerant interaction with the environment. Internally, all representations are fully local. This simplifies weight assignment and makes the networks easy to configure for specific applications. These hybrid distributed/local architectures are especially useful for applications were structured information needs to be represented. Three such applications are briefly discussed: a scheme for knowledge representation, a connectionist rule-based system, and a knowledge-base browser.
| Original language | English (US) |
|---|---|
| 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 |