Moderating the outputs of support vector machine classifiers
Moderating the outputs of support vector machine classifiers
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DOI：
10.1109/ijcnn.1999.831080
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发表时间：
1999-09
期刊：
影响因子：
--
通讯作者：
J. Kwok
中科院分区：
文献类型：
--
作者：
J. Kwok
在本文中，我们扩展了使用温和的输出支持向量机（SVM），利用SVM和证据框架之间的关系。适度的输出更符合贝叶斯的思想，即在预测时应考虑后验权重分布，并且它也消除了通常倾向于对测试模式的估计类成员分配过高的置信度。此外，这里推导出的适度输出可以视为后验类概率的近似值。因此，可以分配有意义的拒绝阈值，并且可以直接比较来自多个网络的输出。人工和真实世界的数据上的实验结果也进行了讨论。
In this paper, we extend the use of moderated outputs to the support vector machine (SVM) by making use of a relationship between SVM and the evidence framework. The moderated output is more in line with the Bayesian idea that the posterior weight distribution should be taken into account upon prediction, and it also alleviates the usual tendency of assigning overly high confidence to the estimated class memberships of the test patterns. Moreover, the moderated output derived here can be taken as an approximation to the posterior class probability. Hence, meaningful rejection thresholds can be assigned and outputs from several networks can be directly compared. Experimental results on both artificial and real-world data are also discussed.