Accession Number:

ADA244080

Title:

Behavior and Learning in Networks with Differing Amounts of Structure

Descriptive Note:

Final rept. 1 Dec 88-30 Nov 90,

Corporate Author:

WISCONSIN UNIV-MADISON DEPT OF COMPUTER SCIENCES

Personal Author(s):

Report Date:

1991-09-09

Pagination or Media Count:

28.0

Abstract:

This research is investigating how well large networks that are built from neuron-like elements can be made to perform, and learn to perform, by giving them different types and amounts of built in structure, and the ability to learn by generating new nodes in additions to changing weights. Substantial improvements in both learning speed and performance have been achieved on both pattern recognition problems and a range of problems typically used to demonstrate the power of connectionist networks. In addition, a number of new micro-circuits and sub-networks have been specified with which more powerful and more flexible networks can be built. These include Back-cycling nets that handle learningalong with many useful functions, rather than have that handled by the system that executes the net Nets that handle symbols as well as numbers and Micro-circuits for productions and perceptual transforms.

Subject Categories:

  • Psychology
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE