A linear classification model based on conditional geometric score
A linear classification model based on conditional geometric score
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发表时间：
2004
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通讯作者：
Jun-ya Gotoh;A. Takeda
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作者：
Jun-ya Gotoh;A. Takeda
我们提出了一个考虑每个数据点到判别超平面的欧氏距离的两类线性分类模型，并引入了一个风险度量，即金融风险管理中的条件风险值。将其表述为一个非凸规划问题，并通过研究该问题的特殊结构，给出了一种求解全局或局部最优解的方法。在一定的参数设置下，证明了该模型与0 -支持向量分类的等价性，数值实验表明，该模型在总体上具有较好的预测精度。
We propose a two-class linear classification model by taking into account the Euclidean distance from each data point to the discriminant hyperplane and introducing a risk measure which is known as the conditional value-at-risk in financial risk management. It is formulated as a nonconvex programming problem and we present a solution method for obtaining either a globally or a locally optimal solution by examining the special structure of the problem. Also, this model is proved to be equivalent to the ν-support vector classification under some parameter setting, and numerical experiments show that the proposed model has better predictive accuracy in general.