Learning from partial correction
We introduce a new model of interactive learning in which an expert examines the predictions of a learner and partially fixes them if they are wrong. Although this kind of feedback is not i.i.d., we show statistical generalization bounds on the quality of the learned model.
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Generalization BoundsSimilar Papers 제목 키워드 기반
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