Improving rule evaluation using multitask learning
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|
|Source:||LNAI 3194: Inductive Logic Programming: The Proceedings of The 14th International Conference on ILP 2004|
|01_Reid_Improving_rule_evaluation_2004.pdf||554.35 kB||Adobe PDF||Request a copy|
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