Venn Prediction (VP) is a new machine learning framework for producing well-calibrated probabilistic predictions. In particular it provides well-calibrated lower and upper bounds for the conditional probability of an example belonging to each possible class of the problem at hand. This paper proposes five VP methods based on Neural Networks (NNs), which is one of the most widely used machine learning techniques. The proposed methods are evaluated experimentally on four benchmark datasets and the obtained results demonstrate the empirical well-calibratedness of their outputs and their superiority over the outputs of the traditional NN classifier.

翻译：维恩预测（VP）是一种新型机器学习框架，用于生成校准良好的概率预测。具体而言，它为样本属于当前问题中每个可能类别的条件概率提供了校准良好的上下界。本文提出了五种基于神经网络（NNs）的VP方法，神经网络是目前最广泛使用的机器学习技术之一。所提方法在四个基准数据集上进行了实验评估，结果表明其输出具有经验上的良好校准性，且优于传统神经网络分类器的输出。