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Title:Universal Transfer Learning
Author(s):Mahmud, M. M. Hassan
Doctoral Committee Chair(s):DeJong, Gerald F.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Artificial Intelligence
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.
Issue Date:2008
Type:Text
Language:English
Description:103 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.
URI:http://hdl.handle.net/2142/81834
Other Identifier(s):(MiAaPQ)AAI3337856
Date Available in IDEALS:2015-09-25
Date Deposited:2008


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