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- W2951863322 abstract "In this work, we study a new approach to optimizing the distribution realized by binary classifiers. The classical approach to this problem is simply maximization of the expected margin, while more recent proposals consider simultaneous variance control and proxy objectives based on robust location estimates, in the vein of keeping the distribution sharply concentrated in a desirable region. While conceptually appealing, these new approaches are often computationally unwieldy, and theoretical guarantees are limited. Given this context, we propose an algorithm which searches the hypothesis space in such a way that a pre-set margin level ends up being a distribution-robust estimator of the location. This procedure is easily implemented using gradient descent, and admits finite-sample bounds on the excess risk under unbounded inputs. Empirical tests on real-world benchmark data reinforce the basic principles highlighted by the theory, and are suggestive of a promising new technique for classification." @default.
- W2951863322 created "2019-06-27" @default.
- W2951863322 creator A5070612925 @default.
- W2951863322 date "2018-10-11" @default.
- W2951863322 modified "2023-09-23" @default.
- W2951863322 title "Classification using margin pursuit" @default.
- W2951863322 hasPublicationYear "2018" @default.
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