@thorob67

Learning to Refine Case Libraries: Initial Results

, and . ECML-97 MLNet Workshop Notes. Case-Based Learning: Beyond Classification of Feature Vectors, page 9--16. Naval Research Laboratory, Washington, D. C., USA, Navy Center for Applied Research in Artificial Intelligence, (1997)

Abstract

Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performance (i.e.\ precision and querying efficiency) is difficult. CBR vendors provide guidelines for designing these libraries manually, but the guidelines are difficult to apply. We describe an automated inductive approach that revises conversational case libraries to increase their conformance with design guidelines. Revision increased performance on three conversational case libraries.

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