PAC-Bayes with Minimax for Confidence-Rated Transduction
Machine Learning
2015-01-19 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1501.03838,
title = {PAC-Bayes with Minimax for Confidence-Rated Transduction},
author = {Akshay Balsubramani and Yoav Freund},
journal= {arXiv preprint arXiv:1501.03838},
year = {2015}
}