We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution using the predicted class distributions of high-confidence validation examples. CAN is easily applicable to any probabilistic classifier, with minimal computation overhead. We analyze the properties of CAN using simulated experiments, and empirically demonstrate its effectiveness across a diverse set of classification tasks.

翻译：我们引入了不同等级的分类(CAN),这是一个非参数化的处理后处理步骤,用于分类。CAN通过使用高信任度验证实例的预测等级分布重新调整其预测的等级概率分布,提高具有挑战性的例子的分类准确性。CAN很容易适用于任何概率分类者,其计算间接费用最小。我们使用模拟实验分析CAN的特性,并用经验证明其在一系列不同分类任务中的有效性。