Models of Learning Systems.

Abstract

The terms adaptation, learning, concept-formation, induction, self-organization, and self-repair have all been used in the context of learning system (LS) research. In this article, three distinct approaches to machine learning and adaptation are considered: (i) the adaptive control approach, (ii) the pattern recognition approach, and (iii) the artificial intelligence approach. Progress in each of these areas is summarized in the first part of the article. In the next part a general model for learning systems is presented that allows characterization and comparison of individual algorithms and programs in all of these areas. The model details the functional components felt to be essential for any learning system, independent of the techniques used for its construction, and the specific environment in which it operates. Specific examples of learning systems are described in terms of the model. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1979
Accession Number
ADA066147

Entities

People

  • Bruce G. Buchanan
  • C. Richard Johnson Jr
  • Reid G. Smith
  • Tom M. Mitchell

Organizations

  • Stanford University

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Adaptive Systems
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Automated Speech Recognition
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computer Vision
  • Computers
  • Control Systems
  • Machine Learning
  • Pattern Recognition
  • Psychology
  • Self Organizing Systems

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML