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首页> 外文期刊>International journal of computational intelligence systems >On implicit Lagrangian twin support vector regression by Newton method
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On implicit Lagrangian twin support vector regression by Newton method

机译：牛顿法求隐式拉格朗日孪生支持向量回归

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摘要

In this work, an implicit Lagrangian for the dual twin support vector regression is proposed. Our formulation leads to determining non-parallel epsilon -insensitive down- and up- bound functions for the unknown regressor by constructing two unconstrained quadratic programming problems of smaller size, instead of a single large one as in the standard support vector regression (SVR). The two related support vector machine type problems are solved using Newton method. Numerical experiments were performed on a number of interesting synthetic and real-world benchmark datasets and their results were compared with SVR and twin SVR. Similar or better generalization performance of the proposed method clearly illustrates its effectiveness and applicability.
机译：在这项工作中，提出了用于双孪生支持向量回归的隐式拉格朗日法。我们的公式通过构造两个较小的无约束二次规划问题，而不是标准支持向量回归（SVR）中的单个较大问题，来为未知回归确定非平行的不依赖ε的上下限函数。使用牛顿法解决了两个相关的支持向量机类型问题。在许多有趣的合成基准和现实基准数据集上进行了数值实验，并将其结果与SVR和Twin SVR进行了比较。所提出方法的相似或更好的泛化性能清楚地说明了其有效性和适用性。

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