标题： 基于先验概率转移的归纳模型选择 显示英文标题

标题： Transductive Model Selection under Prior Probability Shift

摘要： 半监督学习是一种监督机器学习任务，在这种任务中，与传统归纳学习不同，需要标记的未标记数据是一个有限集合，并且在训练时可用。 类似于归纳学习环境，半监督学习环境可能受到数据集偏移的影响，即可能不满足独立同分布假设。 我们在这里提出一种方法，专门针对半监督分类环境，在数据表现出先验概率偏移时进行模型选择（即超参数优化），这是一种典型的反因果学习问题的重要类型。 在我们提出的方法中，超参数可以直接在需要应用训练分类器的未标记数据上进行优化；这与传统的模型选择方法不同，传统的模型选择方法是基于在标记的训练数据上进行交叉验证。 我们提供了实验结果，表明我们的方法带来的好处。 显示英文摘要

摘要： Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are available at training time. Similarly to inductive learning contexts, transductive learning contexts may be affected by dataset shift, i.e., may be such that the IID assumption does not hold. We here propose a method, tailored to transductive classification contexts, for performing model selection (i.e., hyperparameter optimisation) when the data exhibit prior probability shift, an important type of dataset shift typical of anti-causal learning problems. In our proposed method the hyperparameters can be optimised directly on the unlabelled data to which the trained classifier must be applied; this is unlike traditional model selection methods, that are based on performing cross-validation on the labelled training data. We provide experimental results that show the benefits brought about by our method.