Agnostic Learning Of Halfspaces With Gradient Descent Via Soft Margins
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139(2021)
摘要
We analyze the properties of gradient descent on convex surrogates for the zero-one loss for the agnostic learning of halfspaces. We show that when a quantity we refer to as the soft margin is well-behaved-a condition satisfied by log-concave isotropic distributions among others-minimizers of convex surrogates for the zero-one loss are approximate minimizers for the zero-one loss itself. As standard convex optimization arguments lead to efficient guarantees for minimizing convex surrogates of the zero-one loss, our methods allow for the first positive guarantees for the classification error of halfspaces learned by gradient descent using the binary cross-entropy or hinge loss in the presence of agnostic label noise.
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