Date of Award

Spring 2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Modeling & Simulation Engineering

Program/Concentration

Modeling and Simulation

Committee Director

N. Rao Chaganty

Committee Member

Frederic D. McKenzie

Committee Member

Jiang Li

Committee Member

ManWo Ng

Abstract

A logical inference method of properly weighting the outputs of an Artificial Neural Network Committee for predictive purposes using Markov Chain Monte Carlo simulation and Bayesian probability is proposed and demonstrated on machine learning data for non-linear regression, binary classification, and 1-of-k classification. Both deterministic and stochastic models are constructed to model the properties of the data. Prediction strategies are compared based on formal Bayesian predictive distribution modeling of the network committee output data and a stochastic estimation method based on the subtraction of determinism from the given data to achieve a stochastic residual using cross validation. Performance for Bayesian predictive distributions is evaluated using Bayesian methods, while performance for the residual based method is evaluated using conventional statistical techniques.

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In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/6tk8-s779

ISBN

9781303997044

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