Loss Functions
Loss Functions
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DOI：
10.1007/978-3-642-41136-6_8
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
R. C. Williamson
中科院分区：
文献类型：
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作者：
R. C. Williamson
Vapnik描述了模式识别、回归估计和密度估计的“三个主要学习问题”。这些都是根据损失函数定义的损失函数-（用于评估性能）（分别为0-1 lossLoss@0-1 loss、squared loss和log loss）。但是还有很多其他的损失函数可以使用。在本章中，我将总结我和同事最近在损失函数理论方面的研究工作。结果阐明了损失函数集的丰富性，并解释了它们的选择的一些含义。
Vapnik described the “three main learning problems” of pattern recognition, regression estimation and density estimation. These are defined in terms of the loss functionsLoss function—( used to evaluate performance (0-1 lossLoss@0-1 Loss, squared lossSquared loss, and log lossLog loss, respectively). But there are many other loss functions one could use. In this chapter I will summarise some recent work by me and colleagues studying the theoretical aspects of loss functions. The results elucidate the richness of the set of loss functions and explain some of the implications of their choice.