A support vector machine (SVM) classifier corresponds in its most basic form to a quadratic programming problem. Various linear variations of support vector classification have been investigated such as minimizing the L 1 -norm of the weight-vector instead of the L 2 -norm. In this paper we introduce a classifier where we minimize the boundary (lower envelope) of the epigraph that is generated over a set of functions, which can be interpreted as a measure of distance or slack from the origin. The resulting classifier appears to provide a generalization performance similar to SVMs while displaying a more advantageous computational complexity. The discussed formulation can also be extended to allow for cases with imbalanced data.

中文翻译：

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使用线性规划方法的内核分类

支持向量机 (SVM) 分类器的最基本形式对应于二次规划问题。已经研究了支持向量分类的各种线性变化，例如最小化权向量的 L 1 -范数而不是 L 2 -范数。在本文中，我们引入了一个分类器，我们将在一组函数上生成的题词的边界（下包络）最小化，这可以解释为距离原点的距离或松弛的度量。由此产生的分类器似乎提供了类似于 SVM 的泛化性能，同时显示出更有利的计算复杂性。所讨论的公式也可以扩展到允许数据不平衡的情况。