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Cutting-plane methods are well-studied localization(and optimization)algorithms. We show that they provide a natural framework to performmachinelearning ---and not just to solve optimization problems posed bymachinelearning--- in addition to their intended optimization use. Inparticular, theyallow one to learn sparse classifiers and provide goodcompression schemes.Moreover, we show that very little effort is required toturn them intoeffective active learning methods. This last property provides ageneric way todesign a whole family of active learning algorithms from existingpassivemethods. We present numerical simulations testifying of the relevanceofcutting-plane methods for passive and active learning tasks.

From Cutting Planes Algorithms to Compression Schemes and Active Learning