Models of Incremental Concept Formation
Abstract
With a set of observations, humans acquire concepts that organize those observations and use them to classify future experience. This type of concept formation can occur in the absence of a tutor, and despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling the type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation, and then present CLASSIT, a model that extends the earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out research through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions. Keywords: Discrimination network; Conceptual clustering; Machine learning. (KR)
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1989
- Accession Number
- ADA213021
Entities
People
- Douglas Fisher
- John H. Gennari
- Pat Langley
Organizations
- University of California, Irvine