Abstract
The authors apply neural networks to a generalization problem of predicting the ratings of corporate bonds, where conventional mathematical modeling techniques have yielded poor results and it is difficult to build rule-based artificial-intelligence systems. The results indicate that neural nets are a useful approach to generalization problems in such nonconservative domains, performing much better than mathematical modeling techniques like regression.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Int Conf on Neural Networks |
| Publisher | Publ by IEEE |
| Pages | 443-450 |
| Number of pages | 8 |
| State | Published - Dec 1 1988 |