A New Support Vector Machine for Multi-class Classification
A New Support Vector Machine for Multi-class Classification
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DOI：
10.1007/11596448_85
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发表时间：
2005-09
期刊：
影响因子：
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通讯作者：
Zhiquan Qi;Ying-jie Tian;N. Deng
中科院分区：
文献类型：
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作者：
Zhiquan Qi;Ying-jie Tian;N. Deng
用于分类的支持向量机（svm）（简称SVC）在许多现实问题中已被证明是很有前途的分类工具。如何有效地将二值SVC扩展到多类分类中，仍然是一个有待研究的问题。本文在求解K-SVCR算法和nu-K-SVCR算法中的二次规划问题的基础上，引入了线性规划问题。这就产生了一种新的多类问题的算法——k类线性规划支持向量分类回归（K-LSVCR）。在人工数据集和基准数据集上进行的数值实验表明，该方法的效率与K-SVCR和nu-K-SVCR相当，但速度比它们快得多
Support vector machines (SVMs) for classification - in short SVC - have been shown to be promising classification tools in many real-world problems. How to effectively extend binary SVC to multi-class classification is still an on-going research issue. In this article, instead of solving quadratic programming (QP) in algorithm K-SVCR and algorithm nu-K-SVCR, a linear programming (LP) problem is introduced in our algorithm. This leads to a new algorithm for multi-class problem, K-class linear programming support vector classification-regression (K-LSVCR). Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is almost as efficient as K-SVCR and nu-K-SVCR, while considerably faster than them