Accession Number:

ADA289316

Title:

An Analysis of Bayesian Networks as Classifiers.

Descriptive Note:

Master's thesis,

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING

Personal Author(s):

• Ahlquist, Gregory C.

Report Date:

1994-12-01

Pagination or Media Count:

198.0

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 networks 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.

Descriptors:

• *NEURAL NETS
• *DECISION MAKING
• *BAYES THEOREM
• ALGORITHMS
• DATA PROCESSING
• OPTIMIZATION
• PARAMETERS
• PROBABILITY DISTRIBUTION FUNCTIONS
• THESES
• INPUT OUTPUT PROCESSING
• PROBLEM SOLVING
• CLASSIFICATION
• PATTERN RECOGNITION
• ARTIFICIAL INTELLIGENCE

Subject Categories:

• Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE