Experiments with some Algorithms that find Central Solutions for Pattern Classification.

Abstract

In two-class pattern recognition, it is a standard technique to have an algorithm for finding hyperplanes which separates the two classes in a linearly separable training set. The traditional methods find a hyperplane which separates all points in one class from all point in the other, but such a hyperplane is not necessarily centered in the empty space between the two classes. Since a central hyperplane does not favor one class or the other, it should have a lower error rate in classifying new points and is therefore better than a noncentral hyperplane. Six algorithms for finding central hyperplanes are tested on three data sets. Although frequently used in practice, the modified relaxation algorithm is very poor. Three algorithms, which are defined in the paper, are found to be quite good. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1977
Accession Number
ADA049062

Entities

People

  • James R. Slagle

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Classification
  • Computer Science
  • Data Sets
  • Intervals
  • Machine Learning
  • Military Research
  • Numbers
  • Pattern Recognition
  • Probability
  • Probability Density Functions
  • Real Numbers
  • Recognition
  • Statistical Inference
  • Test Sets
  • Training

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects