This work introduces Dirichlet Active Learning (DiAL), a Bayesian-inspired
approach to the design of active learning algorithms. Our framework models
feature-conditional class probabilities as a Dirichlet random field and lends
observational strength between similar features in order to calibrate the
random field. This random field can then be utilized in learning tasks: in
particular, we can use current estimates of mean and variance to conduct
classification and active learning in the context where labeled data is scarce.
We demonstrate the applicability of this model to low-label rate graph learning
by constructing ``propagation operators'' based upon the graph Laplacian, and
offer computational studies demonstrating the method's competitiveness with the
state of the art. Finally, we provide rigorous guarantees regarding the ability
of this approach to ensure both exploration and exploitation, expressed
respectively in terms of cluster exploration and increased attention to
decision boundaries.

这项工作介绍了 Dirichlet 主动学习（DiAL），一种灵感来自贝叶斯的主动学习算法设计方法。我们的框架将特征条件类概率建模为 Dirichlet 随机场，并在相似特征之间提供观测强度以校准该随机场。该随机场可以在学习任务中使用：特别是在标签数据稀缺的情况下，我们可以利用当前的均值和方差估计进行分类和主动学习。我们通过基于图拉普拉斯矩阵的 `` 传播算子 '' 构建了这个模型在低标签率图学习中的适用性，并通过计算实验证明了该方法与最先进技术的竞争力。最后，我们提供了关于这种方法在保证探索和开发能力方面的严格保证，分别以群簇探索和对决策边界的关注增加为表达。