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Learning Confidence Sets using Support Vector Machines
Wenbo Wang · Xingye Qiao

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #106

The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outside the overlap of the two sets is deemed to be from one of the two classes, while the overlap is an ambiguity region which could belong to either class. Instead of plug-in approaches, we propose a support vector classifier to construct confidence sets in a flexible manner. Theoretically, we show that the proposed learner can control the non-coverage rates and minimize the ambiguity with high probability. Efficient algorithms are developed and numerical studies illustrate the effectiveness of the proposed method.

Author Information

Wenbo Wang (Binghamton University)
Xingye Qiao (Binghamton University)

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