Measures of an artificial neural network ANN capability are typically based on the Vapnik-Chernonvekis dimension and its variations. These measures may be underestimating the actual ANN's 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 of an ANN is usually related to the desired characteristics of the problem sets. The mathematical framework has been established in which to express other desired invariant descriptors of a capability measure e.g., V-C dimension uses cardinality. A new invariant is defined on the problem space that softens the hard shattering constraint and yields a new capability measure of ANN's. The theory is given as well as examples that demonstrate this new measure.

Date Published: 21 March 2001
PDF: 9 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421183

Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)