G. Towell, J. Shavlik & M. Noordewier (1990).
Refinement of Approximate Domain Theories by Knowledge-Based Neural Networks. Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 861-866, Boston, MA.
This publication is available in PDF and available in postscript.
The data associated with this publication is available online.
Standard algorithms for explanation-based learning require complete and correct knowledge bases. The KBANN system relaxes this constraint through the use of empirical learning methods to refine approximately correct knowledge. This knowledge is used to determine the structure of an artificial neural network and the weights on its links, thereby making the knowledge accessible for modification by neural learning. KBANN is evaluated by empirical tests in the domain of molecular biology. Networks created by KBANN are shown to be superior, in terms of their ability to correctly classify unseen examples, to randomly initialized neural networks, decision trees, ``nearest neighbor'' matching, and standard techniques reported in the biological literature. In addition, KBANN's networks improve the initial knowledge in biologically interesting ways.
Computer Sciences Department
College of Letters and Science
University of Wisconsin - Madison
INFORMATION ~ PEOPLE ~ GRADS ~ UNDERGRADS ~ RESEARCH ~ RESOURCES
5355a Computer Sciences and Statistics ~ 1210 West Dayton Street, Madison, WI 53706
firstname.lastname@example.org ~ voice: 608-262-1204 ~ fax: 608-262-9777