Multiple birth support vector machine for multi-class classification
Multiple birth support vector machine for multi-class classification
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DOI：
10.1007/s00521-012-1108-x
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发表时间：
2012-08
影响因子：
6
通讯作者：
Zhixia Yang;Y. Shao;Xiang-Sun Zhang
中科院分区：
文献类型：
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作者：
Zhixia Yang;Y. Shao;Xiang-Sun Zhang
针对多类分类问题，提出了一种新的多出生支持向量机（MBSVM）算法，它可以看作是孪生支持向量机的一种扩展。我们的MBSVM已经与几个典型的支持向量机进行了比较。从理论上讲，它的计算复杂度非常低，特别是当类数K很大时。基于我们的MBSVM，MBSVM的对偶问题等价于对称混合线性互补问题，可以直接应用逐次超松弛（SOR）。我们建立我们的SOR算法MBSVM。SOR算法每次处理一个数据点，因此它可以处理不需要驻留在内存中的大型数据集。从实际应用的角度来看，初步的数值试验验证了其精度。
For multi-class classification problem, a novel algorithm, called as multiple birth support vector machine (MBSVM), is proposed, which can be considered as an extension of twin support vector machine. Our MBSVM has been compared with the several typical support vector machines. From theoretical point of view, it has been shown that its computational complexity is remarkably low, especially when the class numberKis large. Based on our MBSVM, the dual problems of MBSVM are equivalent to symmetric mixed linear complementarity problems to which successive overrelaxation (SOR) can be directly applied. We establish our SOR algorithm for MBSVM. The SOR algorithm handles one data point at a time, so it can process large dataset that need no reside in memory. From practical point of view, its accuracy has been validated by the preliminary numerical experiments.