TWO CLASSES OF NON-PARAMETRIC TECHNIQUES FOR PATTERN RECOGNITION AND THEIR ERROR ANALYSIS.

Abstract

Another criterion is added to handle the case when distributional information is lacking. This criterion is to approximate the Bayes Solution based only on the statistics acquired during the learning phase. This criterion approaches the optimum risk decision as the learning phase is increased. Two classes of nonparametric techniques are proposed. Corresponding error analyses for these two techniques are made in order to determine how much is lost by using sub-optimal (i.e., a finite learning phase) decisions. (Author)

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1965
Accession Number
AD0628709

Entities

People

  • Joel Owen

Tags

DTIC Thesaurus Topics

  • Data Science
  • Error Analysis
  • Errors
  • Identification
  • Information Science
  • Learning
  • Mathematics
  • Pattern Recognition
  • Recognition
  • Statistics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms