UPAL：基于无偏池的主动学习
UPAL: Unbiased Pool Based Active Learning
摘要：
在本文中，我们解决的问题池为基础的主动学习，并提供了一种算法，称为UPAL，其工作原理是最小化的无偏估计量的风险，在给定的假设空间的假设。对于线性分类器和平方损失的空间，我们表明，UPAL是等价于指数加权平均预测。利用最近的一些结果，随机矩阵的谱允许我们建立一致的UPAL时，真正的假设是一个线性假设。与Vowpal Wabbit中的主动学习器实现和先前提出的基于池的主动学习器实现的实证比较显示出良好的实证性能和更好的可扩展性。
英文摘要：
In this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothesis in a given hypothesis space. For the space of linear classifiers and the squared loss we show that UPAL is equivalent to an exponentially weighted average forecaster. Exploiting some recent results regarding the spectra of random matrices allows us to establish consistency of UPAL when the true hypothesis is a linear hypothesis. Empirical comparison with an active learner implementation in Vowpal Wabbit, and a previously proposed pool based active learner implementation show good empirical performance and better scalability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
