Muffled Semi-Supervised Learning

2016/05/28 by Akshay Balsubramani, Yoav Freund, Balsubramani, Akshay +1
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Water Systems and Optimization

paper · pdf · doi:10.48550/arxiv.1605.08833

openalex publication_date 2016/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Optimal Binary Classifier Aggregation for General Losses

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