Accession Number : ADA195624

Title :   Models of Incremental Concept Formation.

Descriptive Note : Annual rept. Jul 87-Jun 88,

Corporate Author : CALIFORNIA UNIV IRVINE DEPT OF INFORMATION AND COMPUTER SCIENCE

Personal Author(s) : Gennari, John H ; Langley, Pat ; Fisher, Douglas

PDF Url : ADA195624

Report Date : 06 Jun 1988

Pagination or Media Count : 57

Abstract : Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place 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. This paper reviews three previous models of incremental concept formation and then presents CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search 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: Machine learning;Conceptual clustering. (kr)

Descriptors :   *LEARNING MACHINES, *DATA ACQUISITION, CLUSTERING, HIERARCHIES, HUMANS, LEARNING, MODELS

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE