主动学习成本-性能权衡的Rényi熵界
Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff
摘要：
半监督分类是机器学习中最重要的领域之一，它研究如何将大量未标记数据的统计知识与有限的标记数据相结合，以最大限度地提高整体分类精度。在这种情况下，主动选择要标记的数据的过程被称为主动学习。在本文中，我们开始的非渐近分析的最优策略的半监督分类与积极获得的标记数据。考虑到一个一般的贝叶斯分类模型，我们提供了第一个表征的联合最佳的主动学习和半监督分类政策，在成本性能权衡驱动的标签查询预算（数据项的数量被标记）和整体分类精度。利用最近的研究结果的雷尼熵，我们得到严格的信息理论界这样的主动学习的成本-性能权衡。
英文摘要：
Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the often limited labeled data in order to maximize overall classification accuracy. In this context, the process of actively choosing the data to be labeled is referred to as active learning. In this paper, we initiate the non-asymptotic analysis of the optimal policy for semi-supervised classification with actively obtained labeled data. Considering a general Bayesian classification model, we provide the first characterization of the jointly optimal active learning and semi-supervised classification policy, in terms of the cost-performance tradeoff driven by the label query budget (number of data items to be labeled) and overall classification accuracy. Leveraging recent results on the Rényi Entropy, we derive tight information-theoretic bounds on such active learning cost-performance tradeoff.
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