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|
|Number of pages||8|
|State||Published - Dec 1 1988|