Muffled Semi-Supervised Learning

Akshay Balsubramani; Yoav Freund

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arXiv · 1605.08833

Muffled Semi-Supervised Learning

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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Akshay Balsubramani, Yoav Freund. 2016-05-28. Muffled Semi-Supervised Learning. https://arxiv.org/abs/1605.08833

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