Learning probabilistic relational dynamics for multiple tasks
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. === Includes bibliographical references (p. 57-58). === While large data sets have enabled machine learning algorithms to act intelligently in complex domains, standard machine learni...
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ndltd-MIT-oai-dspace.mit.edu-1721.1-416482019-05-02T15:52:08Z Learning probabilistic relational dynamics for multiple tasks Deshpande, Ashwin Leslie Pack Kaelbling. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. Includes bibliographical references (p. 57-58). 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. by Ashwin Deshpande. M.Eng. 2008-05-19T16:05:07Z 2008-05-19T16:05:07Z 2007 2007 Thesis http://hdl.handle.net/1721.1/41648 219711442 eng 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. http://dspace.mit.edu/handle/1721.1/7582 58 p. application/pdf Massachusetts Institute of Technology |
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Electrical Engineering and Computer Science. |
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Electrical Engineering and Computer Science. Deshpande, Ashwin Learning probabilistic relational dynamics for multiple tasks |
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. === Includes bibliographical references (p. 57-58). === 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. === by Ashwin Deshpande. === M.Eng. |
author2 |
Leslie Pack Kaelbling. |
author_facet |
Leslie Pack Kaelbling. Deshpande, Ashwin |
author |
Deshpande, Ashwin |
author_sort |
Deshpande, Ashwin |
title |
Learning probabilistic relational dynamics for multiple tasks |
title_short |
Learning probabilistic relational dynamics for multiple tasks |
title_full |
Learning probabilistic relational dynamics for multiple tasks |
title_fullStr |
Learning probabilistic relational dynamics for multiple tasks |
title_full_unstemmed |
Learning probabilistic relational dynamics for multiple tasks |
title_sort |
learning probabilistic relational dynamics for multiple tasks |
publisher |
Massachusetts Institute of Technology |
publishDate |
2008 |
url |
http://hdl.handle.net/1721.1/41648 |
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AT deshpandeashwin learningprobabilisticrelationaldynamicsformultipletasks |
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1719030082357428224 |