MACHINE LEARNING PROCESSES APPLIED TO PATTERN RECOGNITION

Abstract

The theory of the principal methods for pattern recognition is summarized. Emphasis is on the division of the whole process into three phases: calibration from a sample set of patterns, derivation of numerical properties, and a decision process for determining the class of a pattern. Several mathematical processes are suggested for finding useful properties, including equations which define invariants to acceptable distortions. It is shown that if an efficient and consistant decision process is used, then the property search is easier since the addition of useless or redundant properties cannot degrade the performance. (Author)

Document Details

Document Type
Technical Report
Publication Date
Dec 14, 1961
Accession Number
AD0282549

Entities

People

  • Arthur E. Laemmel

Organizations

  • New York University Tandon School of Engineering

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Calibration
  • Distortion
  • Equations
  • Identification
  • Learning
  • Machine Learning
  • Pattern Recognition
  • Recognition

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference