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Training One-class Support Vector Machines in the Primal Space

机译:培训在原始空间中的单级支持向量机

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In this paper, we proposed a novel Newton-type solver for one-class support vector machines in the primal space directly. Firstly, utilizing reproducing property of kernel and Huber regression function, original constrained quadratic programming is transformed into approximate unconstrained one, which is continuous and twice differentiable. Then, we give a Newton-type training algorithm to solve it. Further analysis shows the computation complexity of our algorithm is identical with theoretical lower bound for solving one-class support vector machines. In the end, experiments on 9 UCI datasets are done to validate the effectivity of proposed algorithm, and when comparing with dual method (LIBSVM), its produces comparative testing accuracy, better training speed, and less support vectors.
机译:在本文中,我们提出了一种直接在原始空间中的单级支持向量机的新型牛顿型求解器。首先,利用内核和HUBER回归函数的再现特性,原始约束的二次编程转换为近似无约束的二次编程,这是连续且两次可分辨率的。然后,我们提供牛顿型训练算法来解决它。进一步的分析表明,我们的算法的计算复杂性与求解单级支持向量机的理论下限相同。最后,完成了9个UCI数据集的实验以验证所提出的算法的有效性,以及与双方法(Libsvm)进行比较时,其产生比较测试精度,更好的训练速度和更少的支持向量。

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