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Proceedings Paper

Learning Significant Class Descriptions
Author(s): Joseph F. Blumberg; James A. Hendler
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Paper Abstract

A program using a learning-by-examples algorithm creates descriptions that are used to differentiate between two classes of prosthetic devices. The best descriptions are selected by the learning algorithm based on a "significance" bias. This bias is automatically derived by a rule system which deduces a level of significance for each of the learned descriptions. The basis for deriving a level of significance for a class description is dependent upon the relationships between the class attributes. Generalized rules are developed which capitalize on the relationships between attributes of a class description in order to deduce a level of significance. It is further hypothesized that the rules are applicable to any domain in which the relationships between class attributes are known a priori. The exchange of information between the learning-by-examples algorithm and the rule system is outlined. The rules are shown along with the representation structure of the class attributes. The results of utilizing three different biases within the learning-by-examples algorithm are also presented. It is shown that the maximum significance bias, equal cost bias, and minimal significance bias provide decreasingly useful descriptions of the prosthetic devices respectively.

Paper Details

Date Published: 11 May 1987
PDF: 11 pages
Proc. SPIE 0786, Applications of Artificial Intelligence V, (11 May 1987); doi: 10.1117/12.940656
Show Author Affiliations
Joseph F. Blumberg, Planning Research Corporation (United States)
James A. Hendler, University of Maryland (United States)

Published in SPIE Proceedings Vol. 0786:
Applications of Artificial Intelligence V
John F. Gilmore, Editor(s)

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