Robust Training of the Quadratic Classifier

Abstract

The quadratic classifier is one of the most applied parametric classifiers used in pattern recognition. To use this classifier, one trains it by estimating the center and the dispersion of the different classes from the data. These estimates are usually made using sample means and sample covariances. If the data errors are normal, this is the optimal procedure. However, in practical situations where the data are not normal or contain outliers, the training can fail because the estimation procedure is not robust. This technical memorandum describes a robust method of estimating these parameters. This estimation method is much more resistant to outliers and perturbations from the assumed normal distribution than existing methods.

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Document Details

Document Type
Technical Report
Publication Date
Feb 02, 1994
Accession Number
ADA640494

Entities

People

  • Paul R. Kersten

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Covariance
  • Data Science
  • Data Sets
  • Dispersions
  • Distribution Functions
  • Estimators
  • Information Science
  • Machine Learning
  • Nonparametric Statistics
  • Normal Distribution
  • Numerical Analysis
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistics
  • Training

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Computer Vision.
  • Regression Analysis.

Technology Areas

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