Combining Prior Knowledge and Data: Beyond the Bayesian Framework
Epshteyn, Arkady
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Permalink
https://hdl.handle.net/2142/81761
Description
Title
Combining Prior Knowledge and Data: Beyond the Bayesian Framework
Author(s)
Epshteyn, Arkady
Issue Date
2007
Doctoral Committee Chair(s)
Gerald DeJong
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Language
eng
Abstract
We explore this task in three contexts: classification (determining the subject of a newsgroup posting), control (learning to perform tasks such as driving a car up a mountain in simulation), and optimization (optimizing performance of linear algebra operations on different hardware platforms). For the text categorization problem, we introduce a novel algorithm which efficiently integrates prior knowledge into large margin classification. For reinforcement learning, we introduce a novel framework for defining and solving planning problems in terms of qualitative statements about the world. In compiler optimization, Bayesian prior based on an analytic model of hardware is combined with empirical measurements of performance of optimized code to determine the maximum-a-posteriori estimates of the optimization parameters.
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