DATA REDUCTION USING INFORMATION THEORETIC TECHNIQUES.

Abstract

Final results are presented from a mathematical study to improve the overall performance of pattern recognition devices, such as for reconnaissance photograph interpretation. The specific results of this study are: (1) the design of practical pattern recognition systems requires mathematical procedures to account for statistical interdependencies and redundancy. (2) Inadequate experimental data sets are a common situation in practice and quantitative error formulas from sampling theory should be employed to assess and monitor the inadequacy. (3) A data reduction and combining technique was developed and tested, which accounts for the redundancy and data inadequacy. (4) A method of evaluating competing recognizer designs was developed from statistical sampling theory.

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1967
Accession Number
AD0813395

Entities

People

  • G. F. Hughes
  • J. A. Lebo

Tags

DTIC Thesaurus Topics

  • Data Reduction
  • Data Sets
  • Experimental Data
  • Pattern Recognition
  • Photographs
  • Photography
  • Recognition
  • Reconnaissance
  • Redundancy
  • Sampling
  • Statistical Sampling

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference