TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition
TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition
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DOI：
10.1016/j.patcog.2011.03.031
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发表时间：
2011-10
期刊：
影响因子：
--
通讯作者：
X. Peng
中科院分区：
文献类型：
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作者：
X. Peng
提出了一种新的双参数-边缘支持向量机分类方法。该方法借鉴了双支持向量机的思想，通过两个较小规模的支持向量机问题所求解的一对非平行的参数裕度超平面来间接确定分离的超平面。与参数边际支持向量机(PAR-ν-ν)类似，该支持向量机适用于多种情况，特别是当数据具有异方差误差结构时，即噪声强烈依赖于输入值时。但与PAR-ν-支持向量机相比，在学习速度上有一定的优势。在多个人工数据集和基准数据集上的实验结果表明，该方法不仅具有较快的学习速度，而且具有良好的泛化能力。
A novel twin parametric-margin support vector machine (TPMSVM) for classification is proposed in this paper. This TPMSVM, in the spirit of the twin support vector machine (TWSVM), determines indirectly the separating hyperplane through a pair of nonparallel parametric-margin hyperplanes solved by two smaller sized support vector machine (SVM)-type problems. Similar to the parametric-margin ν‐support vector machine (par-ν‐SVM), this TPMSVM is suitable for many cases, especially when the data has heteroscedastic error structure, that is, the noise strongly depends on the input value. But there is an advantage in the learning speed compared with the par-ν‐SVM. The experimental results on several artificial and benchmark datasets indicate that the TPMSVM not only obtains fast learning speed, but also shows good generalization.