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arXiv · 1501.03838

PAC-Bayes with Minimax for Confidence-Rated Transduction

Akshay Balsubramani·Yoav Freund
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Abstract

We consider using an ensemble of binary classifiers for transductive prediction, when unlabeled test data are known in advance. We derive minimax optimal rules for confidence-rated prediction in this setting. By using PAC-Bayes analysis on these rules, we obtain data-dependent performance guarantees without distributional assumptions on the data. Our analysis techniques are readily extended to a setting in which the predictor is allowed to abstain.

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Akshay Balsubramani, Yoav Freund. 2015-01-15. PAC-Bayes with Minimax for Confidence-Rated Transduction. https://arxiv.org/abs/1501.03838

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