Twin support vector machine with Universum data

Twin support vector machine with Universum data
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DOI：
10.1016/j.neunet.2012.09.004
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发表时间：
2012-12
期刊：
Neural networks : the official journal of the International Neural Network Society
影响因子：
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通讯作者：
Zhiquan Qi;Ying-jie Tian;Yong Shi
Zhiquan Qi;Ying-jie Tian;Yong Shi
中科院分区：
其他
文献类型：
--
作者：
Zhiquan Qi;Ying-jie Tian;Yong Shi

文献摘要

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优集被定义为不属于感兴趣分类问题的任何一类的样本，已被证明对监督学习有帮助。在这项工作中，我们设计了一种新的双胞胎支持向量机(简称U-TSVM)，它可以利用UNUSUM数据来提高TSVM的分类性能。与U-支持向量机不同的是，在U-TSVM中，通过使用两个铰链损失函数，将UNUSUM数据定位在一个非平行的不敏感损耗管中，这可以更灵活地利用这些嵌入在UNUSUM数据中的先验知识。实验表明，U-TSVM可以直接提高单独使用已标注数据的标准TSVM的分类精度，并且在大多数情况下优于U-SVM。
The Universum, which is defined as the sample not belonging to either class of the classification problem of interest, has been proved to be helpful in supervised learning. In this work, we designed a new Twin Support Vector Machine with Universum (called U-TSVM), which can utilize Universum data to improve the classification performance of TSVM. Unlike U-SVM, in U-TSVM, Universum data are located in a nonparallel insensitive loss tube by using two Hinge Loss functions, which can exploit these prior knowledge embedded in Universum data more flexible. Empirical experiments demonstrate that U-TSVM can directly improve the classification accuracy of standard TSVM that use the labeled data alone and is superior to U-SVM in most cases.