AN ADAPTIVE HYPERSPHERE DECISION BOUNDARY.

Abstract

It was previously shown that the hypersphere decision boundary optimally partitions an n-dimensional sample space when the underlying category probability distributions are of certain types, including spherically symmetric normal and Pearson Types II and VII. Such a model could apply to pattern recognition or to detection of band-limited white stochastic signals. The hypersphere partition arises for distributions differing in their means and in their variances. This paper examines the problem of adaptation, and treats the following two topics: non-supervised adaptation to the optimum hypersphere for normal distributions; supervised estimation of the Pearson shape parameter m, thereby supplementing the partial treatment of estimation in an earlier hypersphere paper. (Author)

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1966
Accession Number
AD0631083

Entities

People

  • Paul W. Cooper

Organizations

  • Sylvania Electric Products

Tags

DTIC Thesaurus Topics

  • Boundaries
  • Change Detection
  • Detection
  • Mathematics
  • Normal Distribution
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Recognition

Readers

  • Approximation Theory.
  • Graph Algorithms and Convex Optimization.
  • Neural Network Machine Learning.

Technology Areas

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