Machine Learning

, Volume 2, Issue 2, pp 103–138 | Cite as

Experiments with incremental concept formation: UNIMEM

  • Michael Lebowitz
Article

Abstract

Learning by observation involves automatic creation of categories that summarize experience. In this paper we present UNIMEM, an artificial intelligence system that learns by observation. UNIMEM is a robust program that can be run on many domains with real-world problem characteristics such as uncertainty, incompleteness, and large numbers of examples. We give an overview of the program that illustrates several key elements, including the automatic creation of non-disjoint concept hierarchies that are evaluated over time. We then describe several experiments that we have carried out with UNIMEM, including tests on different domains (universities, Congressional voting records, and terrorist events) and an examination of the effect of varying UNIMEM's parameters on the resulting concept hierarchies. Finally we discuss future directions for our work with the program.

Keywords

Concept formation learning by observation generalization conceptual clustering 

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Copyright information

© Kluwer Academic Publishers 1987

Authors and Affiliations

  • Michael Lebowitz
    • 1
  1. 1.Department of Computer ScienceColumbia UniversityNew YorkU.S.A.

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