Accession Number : ADA188531

Title :   CONSENSUS: A Statistical Learning Procedure in a Connectionist Network.

Descriptive Note : Interim rept.,

Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE

Personal Author(s) : Goetsch, Gordon J

PDF Url : ADA188531

Report Date : Dec 1987

Pagination or Media Count : 54

Abstract : This document presents a new scheme for the activity of neuron-like elements in a connectionist network. The CONSENSUS (Context Sensitive Networks Using Statistics), is that decisions should be deferred until sufficient evidence accumulates to make an informed choice. Consequently, large changes in network structure can be made with confidence. Nodes have an awareness of their role and utility in the network which allows them to increase their effectiveness. The reinforcement scheme utilizes the notion of confidence so that only nodes proven to contribute successfully issue reinforcements. Nodes are grouped into communities to exploit their collective knowledge which exceeds any individual member. The network was tested against several problems and was able to find suitable encodings to solve them.

Descriptors :   *NETWORKS, *STATISTICAL PROCESSES, *STATISTICAL INFERENCE, LEARNING, SENSITIVITY, NODES

Subject Categories : Cybernetics
      Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE