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]
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