We prove that for any VC class, it is possible to transform any passive learning algorithm into an active learning algorithm with strong asymptotic improvements in label complexity for every nontrivial distribution satisfying a uniform classification noise condition. This generalizes a similar result proven by (Hanneke, 2009; 2012) for the realizable case, and is the first result establishing that such general improvement guarantees are possible in the presence of restricted types of classification noise.
['Liu Yang', 'Steve Hanneke']
Activized Learning with Uniform Classification Noise