Share Email Print

Proceedings Paper

Stable Optimization With Application To Syntactic Learning
Author(s): Juan A. Herrera; J. Robin B. Cockett
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The recent development of an algebra for the manipulation of decision trees has allowed the implementation of an algorithm for generating all the irreducible forms of a decision tree. An irreducible is a syntactic form for a decision tree which is guaranteed to be optimal for some cost criterion (for example, an expected testing cost). However, each irreducible is optimal only under certain stability conditions. Thus, in the absence of specific costing information, the more demanding the stability conditions for an irreducible, the less generally useful the tree. This paper illustrates, by means of an example, a syntactic approach to decision tree inference in which all the irreducible decision trees which are consistent with respect to a given set of training examples are generated, and a test for stability is used to narrow down the selection of a reasonable inference model.

Paper Details

Date Published: 29 March 1988
PDF: 8 pages
Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); doi: 10.1117/12.946982
Show Author Affiliations
Juan A. Herrera, Perceptics Corporation (United States)
University of Tennessee Knoxville (United States)
J. Robin B. Cockett, University of Tennessee Knoxville (United States)

Published in SPIE Proceedings Vol. 0937:
Applications of Artificial Intelligence VI
Mohan M. Trivedi, Editor(s)

© SPIE. Terms of Use
Back to Top