Accession Number:

ADA238003

Title:

Neuron Requirements for Classification

Descriptive Note:

Final rept. May 1989-Jul 1990

Corporate Author:

NAVAL WEAPONS CENTER CHINA LAKE CA

Personal Author(s):

Report Date:

1991-01-01

Pagination or Media Count:

34.0

Abstract:

The feed forward layered neural networks holds great promise for application to classification problems. Determination of the sizes of the layers is an important network design problem. This report treats the neuron requirement question from the geometric viewpoint. Threshold neurons correspond to cutting planes in the Euclidean space of input patterns. Bounds on the minimum number of first-layer neurons are determined as functions of the partition sizes of the training data sets. Bounds are also proved for convex pattern classes. Measures of separability of the training data are defined in order to emphasize the dependence of the design parameters upon the geometry of the classes.

Subject Categories:

  • Operations Research
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE