Moderating the outputs of support vector machine classifiers
Moderating the outputs of support vector machine classifiers
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DOI：
10.1109/72.788642
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发表时间：
1999-09-01
影响因子：
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通讯作者：
Kwok, JTY
中科院分区：
文献类型：
--
作者：
Kwok, JTY
在本文中，我们通过使用支持向量机和证据框架之间的关系，将有调节输出的使用扩展到支持向量机（SVM）。经过调节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，它也缓解了通常对测试模式的估计类隶属度分配过高置信度的倾向。此外，这里导出的缓和输出可以作为后验类概率的近似值。因此，可以分配有意义的拒绝阈值，并且可以直接比较几个网络的输出，还讨论了人工和现实数据的实验结果。
In this paper, we extend the use of moderated outputs to the support vector machine (SVM) by malting use of a relationship between SVM and the evidence framework. Tie 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.