dc.contributor.advisor | Leslie Pack Kaelbling. | en_US |
dc.contributor.author | Deshpande, Ashwin | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2008-05-19T16:05:07Z | |
dc.date.available | 2008-05-19T16:05:07Z | |
dc.date.copyright | 2007 | en_US |
dc.date.issued | 2007 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/41648 | |
dc.description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. | en_US |
dc.description | Includes bibliographical references (p. 57-58). | en_US |
dc.description.abstract | While large data sets have enabled machine learning algorithms to act intelligently in complex domains, standard machine learning algorithms perform poorly in situations in which little data exists for the desired target task. Transfer learning attempts to extract trends from the data of similar source tasks to enhance learning in the target task. We apply transfer learning to probabilistic rule learning to learn the dynamics of a target world. We utilize a hierarchical Bayesian framework and specify a generative model which dictates the probabilities of task data, task rulesets and a common global ruleset. Through a greedy coordinated-ascent algorithm, the source tasks contribute towards building the global ruleset which can then be used as a prior to supplement the data from the target ruleset. Simulated experimental results in a variety of blocks-world domains suggest that employing transfer learning can provide significant accuracy gains over traditional single task rule learning algorithms. | en_US |
dc.description.statementofresponsibility | by Ashwin Deshpande. | en_US |
dc.format.extent | 58 p. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Learning probabilistic relational dynamics for multiple tasks | en_US |
dc.type | Thesis | en_US |
dc.description.degree | M.Eng. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.identifier.oclc | 219711442 | en_US |