Accession Number : ADA297408
Title : The Mathematics of Measuring Capabilities of Artificial Neural Networks.
Descriptive Note : Doctoral thesis,
Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH
Personal Author(s) : Carter, Martha A.
PDF Url : ADA297408
Report Date : JUN 1995
Pagination or Media Count : 121
Abstract : Researchers rely on the mathematics of Vapnik and Chervonenkis to capture quantitatively the capabilities of specific artificial neural network (ANN) architectures. The quantifier is known as the V-C dimension, and is defined on functions or sets. Its value is the largest cardinality 1 of a set of vectors in Rd such that there is at least one set of vectors of cardinality 1 such that all dichotomies of that set into two sets can be implemented by the function or set. Stated another way, the V-C dimension of a set of functions is the largest cardinality of a set, such that there exists one set of that cardinality which can be shattered by the set of functions. A set of functions is said to shatter a set if each dichotomy of that set can be implemented by a function in the set. There is an abundance of research on determining the value of V-C dimensions of ANNs. In this document, research on V-C dimension is refined and extended yielding formulas for evaluating V-C dimension for the set of functions representable by a feed-forward, single hidden-layer perceptron artificial neural network.The fundamental thesis of this research is that the V-C dimension is not an appropriate quantifier of ANN capabilities. (KAR) P. 11
Descriptors : *MEASUREMENT, *NEURAL NETS, *MATHEMATICAL ANALYSIS, FUNCTIONS, THESES, INVARIANCE.
Subject Categories : Numerical Mathematics
Distribution Statement : APPROVED FOR PUBLIC RELEASE