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Receiver operating characteristics of perceptrons: Influence of sample size and prevalence

机译:感知器的接收器工作特性:样本量和患病率的影响

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摘要

In many practical classification problems it is important to distinguish false positive from false negative results when evaluating the performance of the classifier. This is of particular importance for medical diagnostic tests. In this context, receiver operating characteristic (ROC) curves have become a standard tool. Here we apply this concept to characterize the performance of a simple neural network. Investigating the binary classification of a perceptron we calculate analytically the shape of the corresponding ROC curves. The influence of the size of the training set and the prevalence of the quality considered are studied by means of a statistical-mechanics analysis.
机译:在许多实际的分类问题中,在评估分类器的性能时,区分假阳性和假阴性结果很重要。这对于医学诊断测试特别重要。在这种情况下,接收器工作特性(ROC)曲线已成为标准工具。在这里,我们将这一概念应用于表征简单神经网络的性能。研究感知器的二进制分类,我们分析计算了相应ROC曲线的形状。训练集大小的影响和所考虑质量的普遍性通过统计力学分析的方法进行研究。

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