Weighted Parzen Windows for Pattern Classification

Abstract

This thesis presents a novel pattern recognition approach, named Weighted Parzen Windows (WPW). This technique uses a nonparametric supervised learning algorithm to estimate the underlying density function for each set of training data. Classification is accomplished by using the estimated density functions in a minimum risk strategy. The proposed approach reduces the effective size of the training data without introducing significant classification error. Furthermore, it is shown that Bayes-Gaussian, minimum Euclidean-distance, Parzen-window, and nearest-neighbor classifiers can be viewed as special cases of the WPW technique. Experimental results are presented to demonstrate the performance of the WPW algorithm as compared to traditional classifiers. Parzen Windows, Weighted, Pattern, Recognition, Classification, Algorithm

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1994
Accession Number
ADA281222

Entities

People

  • G. A. Babich
  • L. H. Sibul

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Classification
  • Computational Complexity
  • Data Science
  • Discriminant Analysis
  • Feature Extraction
  • Gaussian Distributions
  • Information Science
  • Learning
  • Machine Learning
  • Neural Networks
  • Parallel Computing
  • Pattern Recognition
  • Probability Density Functions
  • Recognition
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference