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https://hdl.handle.net/2142/81834
Description
Title
Universal Transfer Learning
Author(s)
Mahmud, M.M. Hassan
Issue Date
2008
Doctoral Committee Chair(s)
DeJong, Gerald F.
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
Our distance measures and learning algorithms are based on powerful, elegant and beautiful ideas from the field of Algorithmic Information Theory. While developing our transfer learning mechanisms we also derive results that are interesting in and of themselves. We also developed practical approximations to our formally optimal method for Bayesian decision trees, and applied it to transfer information between 7 arbitrarily chosen data-sets in the UCI machine learning repository through a battery of 144 experiments. The arbitrary choice of databases makes our experiments the most general transfer experiments to date. The experiments also bear out our result that transfer should never hurt too much.
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