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• W2950197242 abstract "The disagreement coefficient of Hanneke has become a central data independent invariant in proving active learning rates. It has been shown in various ways that a concept class with low complexity together with a bound on the disagreement coefficient at an optimal solution allows active learning rates that are superior to passive learning ones. We present a different tool for pool based active learning which follows from the existence of a certain uniform version of low disagreement coefficient, but is not equivalent to it. In fact, we present two fundamental active learning problems of significant interest for which our approach allows nontrivial active learning bounds. However, any general purpose method relying on the disagreement coefficient bounds only fails to guarantee any useful bounds for these problems. The tool we use is based on the learner's ability to compute an estimator of the difference between the loss of any hypotheses and some fixed pivotal hypothesis to within an absolute error of at most $eps$ times the" @default.
• W2950197242 created "2019-06-27" @default.
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• W2950197242 date "2011-10-10" @default.
• W2950197242 modified "2023-09-27" @default.
• W2950197242 title "Active Learning Using Smooth Relative Regret Approximations with Applications" @default.
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• W2950197242 hasPublicationYear "2011" @default.
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