Improving rule evaluation using multitask learning
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Description
This paper introduces DEFT, a new multitask learning approach for rule learning algorithms. Like other multitask learning systems, the one proposed here is able to improve learning performance on a primary task through the use of a bias learnt from similar secondary tasks. What distinguishes DEFT from other approaches is its use of rule descriptions as a basis for task similarity. By translating a rule into a feature vector or "description", the performance of similarly described rules on the...[Show more]
Collections | ANU Research Publications |
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Date published: | 2004 |
Type: | Conference paper |
URI: | http://hdl.handle.net/1885/58255 |
Source: | LNAI 3194: Inductive Logic Programming: The Proceedings of The 14th International Conference on ILP 2004 |
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File | Description | Size | Format | Image |
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01_Reid_Improving_rule_evaluation_2004.pdf | 554.35 kB | Adobe PDF |
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