We explore the potential for using a nonsmooth loss function based on the max-norm in the training of an artificial neural network without hidden layers. We hypothesise that this may lead to superior classification results in some special cases where the training data are either very small or the class size is disproportional. Our numerical experiments performed on a simple artificial neural network with no hidden layer appear to confirm our hypothesis.

中文翻译：

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具有统一的基于范数的损失函数的人工神经网络

我们探索在没有隐藏层的人工神经网络训练中使用基于最大范数的非平滑损失函数的潜力。我们假设，在训练数据非常小或类别大小不成比例的某些特殊情况下，这可能会带来更好的分类结果。我们在没有隐藏层的简单人工神经网络上进行的数值实验似乎证实了我们的假设。