Computer Science ETDs

Publication Date

5-1-2014

Abstract

Combinatorial optimization problems occur in a wide range of domains, from Bayesian network structure search to questions in neuroscience and biochemistry. However, all of these problems have in common the need to optimize some score, and often the calculation of this score is a significant source of slowness in the search for a solution. Through the use of a carefully calibrated approximation, however, this time can be significantly reduced with little effect on the quality of the results. I demonstrate here how such a proxy function can be used, as well as explore situations where the proxy strategy fails and offer reasons why or why not it might be suited to a particular problem.

Language

English

Keywords

bayesian, combinatoric optimization, optimization, machine learning, learning

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Advisor

Luger, George

First Committee Member (Chair)

Lane, Terran

Second Committee Member

Williams, Lance R.

Third Committee Member

Guindani, Michele

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