计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (31): 57-59.DOI: 10.3778/j.issn.1002-8331.2008.31.016
• 理论研究 • 上一篇 下一篇
刘晓红
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LIU Xiao-hong
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摘要： 线性支持向量机的无约束优化模型的目标函数不是一个二阶可微函数，因此不能应用一些快速牛顿算法来求解。提出了目标函数的一种光滑化技巧，从而得到了相应的光滑线性支持向量机模型，并给出了求解该光滑线性支持向量机模型的Newton-Armijo算法，该算法是全局收敛的和二次收敛的。
关键词: 机器学习, 支持向量机, 光滑算法
Abstract: The objective function in the unconstrained model of linear Support Vector Machine（SVM） is not twice differentiable which precludes the use of a fast Newton method.In this paper，a type of smoothing technique is proposed to overcome the difficulty.By using the smoothing technique，a smoothing SVM model is obtained.A Newton-Armijo algorithm which is globally and quadratically convergent is given to solve the smoothing SVM model.
Key words: machine learning, Support Vector Machine（SVM）, smoothing type algorithm
刘晓红. 线性支持向量机优化问题的一种光滑算法[J]. 计算机工程与应用, 2008, 44(31): 57-59.
LIU Xiao-hong. Smoothing algorithm for linear Support Vector Machine[J]. Computer Engineering and Applications, 2008, 44(31): 57-59.
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链接本文: http://cea.ceaj.org/CN/10.3778/j.issn.1002-8331.2008.31.016
http://cea.ceaj.org/CN/Y2008/V44/I31/57
