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Scalable Nonlinear AUC Maximization Methods
[article]
2019
arXiv
pre-print
The area under the ROC curve (AUC) is a measure of interest in various machine learning and data mining applications. It has been widely used to evaluate classification performance on heavily imbalanced data. The kernelized AUC maximization machines have established a superior generalization ability compared to linear AUC machines because of their capability in modeling the complex nonlinear structure underlying most real-world data. However, the high training complexity renders the kernelized
arXiv:1710.00760v4
fatcat:dqpgvvabyjcczbk4373ovsctjq