Abstract
dc:descriptionOur 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.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Mahmud, M.M. Hassan
- Contributors dc:contributor
-
- DeJong, Gerald F.
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3337856
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81834