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Proceedings Paper

Generalized measures of artificial neural network capabilities
Author(s): Martha Alvey Carter; Mark E. Oxley
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Paper Abstract

Current measures of an artificial neural networks (ANN) capability are the V-C dimension and its variations. These measures may be underestimating capabilities (in the primal sense) and hence overestimating the required number of examples for learning (in the dual sense). This is a result of relying on a single invariant description of the problem set, which is cardinality, and requiring worst case geometries and colorings. Generalization of a capability measure allows aligning the measure with desired characteristics of the problem sets. We present a mathematical framework in which to express other desired invariant descriptors of a capability measure, and guarantee proper application of the measure to ANNs. We define a collection of invariants defined on the problem space that yield new capability measures of ANNs. A specific example of an invariant is given which is based on geometric complexity of the problem set and yields a new measure of ANNs called the Ox-Cart dimension.

Paper Details

Date Published: 22 March 1999
PDF: 12 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342875
Show Author Affiliations
Martha Alvey Carter, National Air Intelligence Ctr. (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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