Support vector machines for multi-class pattern recognition
Support vector machines for multi-class pattern recognition
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发表时间：
1999
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通讯作者：
J. Weston;C. Watkins
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作者：
J. Weston;C. Watkins
.使用支持向量机（SVM）的二进制分类问题的解决方案已经发展得很好，但是具有多于两个类的多类问题通常通过组合独立产生的二进制分类器来解决。我们提出了一个公式的SVM，使多类模式识别问题，以解决在一个单一的优化。我们还提出了一个类似的线性规划机的推广。我们报告使用基准数据集的实验，其中这两种方法实现了所需的支持向量和内核计算的数量减少
. The solution of binary classi(cid:12)cation problems using support vector machines (SVMs) is well developed, but multi-class problems with more than two classes have typically been solved by combining independently produced binary classi(cid:12)ers. We propose a formulation of the SVM that enables a multi-class pattern recognition problem to be solved in a single optimisation. We also propose a similar generalization of linear programming machines. We report experiments using bench-mark datasets in which these two methods achieve a reduction in the number of support vectors and kernel calculations needed