Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that measures the usefulness of the unlabeled data. We propose a novel approach which we refer to as maximizing variance for active learning or MVAL for short. MVAL measures the value of unlabeled instances by evaluating the rate of change of output variables caused by changes in the next sample to be queried and its potential labelling. In a sense, this criterion measures how unstable the classifier's output is for the unlabeled data points under perturbations of the training data. MVAL maintains, what we will refer to as, retraining information matrices to keep track of these output scores and exploits two kinds of variance to measure the informativeness and representativeness, respectively. By fusing these variances, MVAL is able to select the instances which are both informative and representative. We employ our technique both in combination with logistic regression and support vector machines and demonstrate that MVAL achieves state-of-the-art performance in experiments on a large number of standard benchmark datasets.

提出了一种新的主动学习方法 MVAL，通过评估下一个待查询样本和其潜在标记的变化引起的输出变量的变化率来测量未标记实例的价值，并利用信息矩阵来跟踪这些输出分数，通过融合方差来选择既具有信息性又代表性的示例。与逻辑回归和支持向量机相结合，表现出与同类算法相比的最先进性能。

主动学习的方差最大化准则