In this paper, we present a novel SVM-based approach to construct multiclass classifiers by means of arrangements of hyperplanes. We propose different mixed integer (linear and non linear) programming formulations for the problem using extensions of widely used measures for misclassifying observations where the kernel trick can be adapted to be applicable. Some dimensionality reductions and variable fixing strategies are also developed for these models. An extensive battery of experiments has been run which reveal the powerfulness of our proposal as compared with other previously proposed methodologies.

中文翻译：

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基于SVM的多类分类的超平面的最佳布置

在本文中，我们提出了一种新的基于SVM的方法，该方法通过超平面的布置来构造多类分类器。我们使用广泛使用的措施的扩展为问题提出了不同的混合整数（线性和非线性）编程公式，这些措施用于对观察值进行错误分类，其中内核技巧可以适用。还为这些模型开发了一些降维和变量固定策略。已经进行了广泛的实验，这些实验揭示了我们的建议与以前提出的其他方法相比的强大功能。