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Proceedings Paper

Topological-based capability measures of artificial neural network architectures
Author(s): Mark E. Oxley; Martha Alvey Carter
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Paper Abstract

Current measures of an artificial neural network (ANN) capability are based on the V-C dimension and its variations. These measures may be underestimating the actual ANNs capabilities and hence overestimating the required number of examples for learning. This is caused by relying on a single invariant description of the problem set, which, in this case is cardinality, and requiring worst case geometric arrangements and colorings. A capability measure allows aligning the measure with desired characteristics of the problem sets. The mathematical framework has been established in which to express other desired invariant descriptors of a capability measure. New invariants are defined on the problem space that yield new capability measures of ANNs that are based on topological properties. A specific example of an invariant is given which is based on topological properties of the problem set and yields a new measure of ANN architecture.

Paper Details

Date Published: 30 March 2000
PDF: 9 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380558
Show Author Affiliations
Mark E. Oxley, Air Force Institute of Technology (United States)
Martha Alvey Carter, National Air Intelligence Ctr. (United States)


Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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