PATTERN CLASSIFICATION WITH A PARTITIONED TRAINING SET,

Abstract

Pattern classification can be considered as consisting of two parts: (1) Pattern detection - The process of learning from a set of sample patterns of known classifications and discriminating characteristics of each category; and (2) Actual classification - The process of recognizing patterns of unknown classifications as members of particular categories. The paper is a study in the first part of the process since it is most often te more important part of any pattern classification scheme. An algorithm for establishing decision criteria of classification is described. Evaluation is made on its performance, computation time and data storage requirement. (Author)

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1970
Accession Number
AD0704651

Entities

People

  • James Cheh-min Chow

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computational Complexity
  • Computations
  • Data Storage Systems
  • Detection
  • Learning
  • Mathematical Analysis
  • Mathematics
  • Test And Evaluation
  • Training

Readers

  • Computer Vision.
  • Systems Analysis and Design