DECISION MAKING NETWORKS IN PATTERN RECOGNITION,

Abstract

A feed forward switching network is described and then used as a basis for a pattern classifier. Rules associated with the network direct an arbitrary signal from a starting node to one of a group of terminal nodes, each of which is identified with a pattern class. The empirical distribution functions of a training set of pattern samples determine the manner in which the rules are constructed. The rules are functions of the input pattern binary variables. It is shown that, for a class of networks having the ordering property and statistically ordered rules at each node, increasing the size of the network decreases the probability of error. (Author)

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1969
Accession Number
AD0695413

Entities

People

  • Leonard J. Grantner

Organizations

  • Columbia University

Tags

DTIC Thesaurus Topics

  • Distribution Functions
  • Identification
  • Machine Learning
  • Mathematics
  • Pattern Recognition
  • Probability
  • Recognition
  • Switching
  • Terminals
  • Training

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks