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Muffled Semi-Supervised Learning

2016-05-28Code Available0· sign in to hype

Akshay Balsubramani, Yoav Freund

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Abstract

We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than boosted trees, random forests and logistic regression when unlabeled examples are available.

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