An Analysis of Bayesian Networks as Classifiers.

Abstract

An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm and several tools related to Bayesian network classifiers. The tools calculate and display the decision regions for two level Bayesian network classifiers. They collectively provide an approach to analyze the effects of changing network parameters on the network's decision regions. The algorithm defines a Bayesian network classifier to solve traditional classification problems. The algorithm is data driven, meaning that the resulting Bayesian network classifier is uniquely tuned to the classification problem at hand. Also, the algorithm contains procedures for defining the topology of a Bayesian network classifier and for precisely deriving the required conditional probabilities. A brief tutorial on Bayesian networks is also presented.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1994
Accession Number
ADA289316

Entities

People

  • Gregory C. Ahlquist

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Classification
  • Computational Science
  • Computer Graphics
  • Data Sets
  • Gaussian Distributions
  • Machine Learning
  • Probability
  • Probability Distributions
  • Propulsion Systems
  • Synthetic Aperture Radar
  • Target Recognition
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Computer Vision.

Technology Areas

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