Non Parametric Classification Using Learning Vector Quantization

Abstract

We study several properties of Learning Vector Quantization (LVQ). LVQ is a nonparametric detection scheme proposed in the neural network community by Kohonen. We examine it in detail, both theoretical and experimentally, to determine its properties as a nonparametric classifier. In particular, we study the convergence of the parameter adjustment rule in LVQ, we present a modification to LVQ which results in improving he convergence of the algorithm, we show that LVQ performs as well as other classifiers on two sets of a simulations, and we show that the classification error associated with LVQ can be made arbitrarily small. (r.r.h.)

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Document Details

Document Type
Technical Report
Publication Date
Aug 21, 1990
Accession Number
ADA226131

Entities

People

  • Anthony Lavigna

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Sets
  • Detection
  • Detectors
  • Differential Equations
  • Equations
  • Estimators
  • Information Science
  • Machine Learning
  • Markov Processes
  • Neural Networks
  • New York
  • Pattern Recognition
  • Probability
  • Random Variables
  • Signal Processing

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks