We address the problem of aggregating an ensemble of predictors with known
loss bounds in a semi-supervised binary classification setting, to minimize
prediction loss incurred on the unlabeled data. We find the minimax optimal
predictions for a very general class of loss functions including all convex and
many non-convex losses, extending a recent analysis of the problem for
misclassification error. The result is a family of semi-supervised ensemble
aggregation algorithms which are as efficient as linear learning by convex
optimization, but are minimax optimal without any relaxations. Their decision
rules take a form familiar in decision theory -- applying sigmoid functions to
a notion of ensemble margin -- without the assumptions typically made in
margin-based learning.

本文旨在解决半监督二分类集合聚合问题，以最小化在未标记数据上产生的预测损失，并找到了一类最小 - 最大最优预测。结果是一组半监督集合聚合算法，能像线性学习一样高效，但无需放松任何限制。它们的决策规则采用决策理论中熟悉的形式，将 Sigmoid 函数应用于集合边缘的概念，而不需要通常在基于边缘的学习中做出的假设。