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Learning from partial correction

By Sanjoy Dasgupta and Michael Luby

Abstract

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.Comment: 13 pages, 2 figure

Topics: Computer Science - Machine Learning
Year: 2018
OAI identifier: oai:arXiv.org:1705.08076

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