THE SYNTHESIS OF MACHINES WHICH LEARN WITHOUT A TEACHER,

Abstract

Techniques of decision theory are applied to the problem of learning to recognize patterns without a teacher. As a result a generalized a posteriori probability computer is obtained which includes the solution of the problem of learning without a teacher, learning with a teacher, and no learning. The resulting equations are shown to describe a system which may be synthesized in delay feedback form, of fixed size, which is stable and converges to that system which would be optimum if a priori knowledge was available so that no learning was required. The solution is used to synthesize three systems in black box form: (1) a general system which learns to make binary decisions, a specific example of this system, and (3) a general system which learns to make multiple-category classifications. (Author)

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1964
Accession Number
AD0443109

Entities

People

  • S. C. Fralick

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Classification
  • Computers
  • Decision Theory
  • Equations
  • Feedback
  • Learning
  • Probability

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Calculus or Mathematical Analysis
  • Statistical inference.