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The Relationship Between Agnostic Selective Classification and Active Learning
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Roei Gelbhart, M.Sc. Thesis Seminar
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Sunday, 19.3.2017, 15:00
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Taub 601
A selective classifier (f,g) consists of a classification function f and a binary selection function g, which determines if the classifier abstains from prediction, or uses f to predict.The classifier is called pointwise-competitive if it classifies each point identically to the best classifier in hindsight (from the same class), whenever it does not abstain. The quality of such a classifier is quantified by its rejection mass, defined to be the probability mass of the of the points it rejects. A ``fast'' rejection rate is achieved if the rejection mass is bounded from above by O(1/m) where m is the number of labeled examples used to train the classifier (and O() hides logarithmic factors).Pointwise-competitive selective (PCS) classifiers are intimately related to disagreement-based active learning and it is known that in the realizable case, a fast rejection rate of a known PCS algorithm (called Consistent Selective Strategy) is equivalent to an exponential speedup of the well known CAL active algorithm. We focus on the agnostic setting, for which there is a known algorithm called LESS that learns a PCS classifier and achieves a fast rejection rate (depending on Hanneke
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