论文标题
Chernoff抽样进行主动测试和扩展到主动回归
Chernoff Sampling for Active Testing and Extension to Active Regression
论文作者
论文摘要
主动学习可以减少执行假设检验所需的样本数量并估算模型的参数。在本文中，我们重新审视了切尔诺夫（Chernoff）的工作，该工作描述了用于执行假设检验的渐近最佳算法。我们获得了Chernoff算法结合的新型样品复杂性，其非反应术语表征其在固定置信度水平上的性能。我们还开发了Chernoff抽样的扩展，该扩展可用于估计各种模型的参数，并在估计误差上获得了一个非反应约束。我们将Chernoff抽样的扩展应用于积极学习神经网络模型并估算实际数据线性和非线性回归问题中的参数，在这些问题中，我们的方法对最新方法有利地表现。
Active learning can reduce the number of samples needed to perform a hypothesis test and to estimate the parameters of a model. In this paper, we revisit the work of Chernoff that described an asymptotically optimal algorithm for performing a hypothesis test. We obtain a novel sample complexity bound for Chernoff's algorithm, with a non-asymptotic term that characterizes its performance at a fixed confidence level. We also develop an extension of Chernoff sampling that can be used to estimate the parameters of a wide variety of models and we obtain a non-asymptotic bound on the estimation error. We apply our extension of Chernoff sampling to actively learn neural network models and to estimate parameters in real-data linear and non-linear regression problems, where our approach performs favorably to state-of-the-art methods.
