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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >An improved nu-twin bounded support vector machine
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An improved nu-twin bounded support vector machine

机译：改进的Nu-Twin界限支持向量机

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摘要

In this paper, we propose a new classifier termed as an improved nu-twin bounded support vector machine (I nu-TBSVM) which is motivated by nu-twin support vector machine (nu-TSVM). Similar to the nu-TSVM, I nu-TBSVM determines two nonparallel hyperplanes such that they are closer to their respective classes and are at least rho (+) or rho (-) distance away from the other class. The significant advantage of I nu-TBSVM over nu-TSVM is that I nu-TBSVM skillfully avoids the expensive matrix inverse operation when solving the dual problems. Therefore, the proposed classifier is more effective when dealing with large scale problem and has comparable generalization ability. I nu-TBSVM also implements structural risk minimization principle by introducing a regularization term into the objective function. More importantly, the kernel trick can be applied directly to the I nu-TBSVM for nonlinear case, so the nonlinear I nu-TBSVM is superior to the nonlinear nu-TSVM theoretically. In addition, we also prove that nu-SVM is the special case of I nu-TBSVM. The property of parameters in I nu-TBSVM is discussed and testified by two artificial experiments. Numerical experiments on twenty-two benchmarking datasets are performed to investigate the validity of our proposed algorithm in both linear case and nonlinear case. Experimental results show the effectiveness of our proposed algorithm.
机译：在本文中，我们提出了一种称为改进的Nu-Twin限定支持向量机（I Nu-TBSVM）的新分类器，其由Nu-Twin支持向量机（NU-TSVM）激励。与Nu-Tsvm类似，i nu-tbsvm确定两个非平行的超平面，使得它们更接近它们各自的类，并且至少远离另一类rho（+）或rho（ - - ）距离。 I Nu-TBSVM OVER OVER NU-TSVM的显着优点是，在解决双重问题时，我巧妙地避免昂贵的矩阵逆操作。因此，在处理大规模问题时，所提出的分类器更有效，具有可比的概括能力。 I Nu-TBSVM还通过将正则化术语引入目标函数来实现结构风险最小化原理。更重要的是，内核技巧可以直接应用于IN-TBSVM的非线性情况，因此非线性I Nu-TBSVM理论上优于非线性Nu-Tsvm。此外，我们还证明了Nu-SVM是I Nu-TBSVM的特殊情况。通过两个人工实验讨论了I Nu-TBSVM中的参数属性。执行二十二个基准测试数据集的数值实验，以研究我们在线性外壳和非线性情况下所提出的算法的有效性。实验结果表明了我们所提出的算法的有效性。

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