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Smooth support vector machine based on piecewise function
  • ISSN号：1024-123X
  • 期刊名称：The Journal of China Universities of Posts and Tel
  • 时间：2013.10.31
  • 页码：122-128
  • 分类：O221.2[理学—运筹学与控制论;理学—数学] TP181[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
  • 作者机构：[1]School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, China, [2]School of telecommunication and information engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
  • 相关基金：Acknowledgements This work was supported by the National Natural Science Foundation of China （61100165, 61100231, 61105064, 51205309）,the Natural Science Foundation of Shaanxi Province （2012JQ8044, 2011JMS003, 2010JQ8004）, and the Foundation of Education Department of Shanxi Province （20133K1096）.
  • 相关项目：标准模型下可证明安全的新型分级身份基加密研究
作者： Qing　Wu|Jiulun Fan|
关键词： 支持向量机, 非光滑, 分段函数, 基础, 光滑逼近函数, 目标函数, 分段光滑, 函数构造, SVM, smooth technique, piecewise function, bound of convergence
中文摘要：

Support vector machines(SVMs)have shown remarkable success in many applications.However,the non-smooth feature of objective function is a limitation in practical application of SVMs.To overcome this disadvantage,a twice continuously differentiable piecewise-smooth function is constructed to smooth the objective function of unconstrained support vector machine(SVM),and it issues a piecewise-smooth support vector machine(PWESSVM).Comparing to the other smooth approximation functions,the smooth precision has an obvious improvement.The theoretical analysis shows PWESSVM is globally convergent.Numerical results and comparisons demonstrate the classification performance of our algorithm is better than other competitive baselines.

英文摘要：

Support vector machines （SVMs） have shown remarkable success in many applications. However, the non-smooth feature of objective function is a limitation in practical application of SVMs. To overcome this disadvantage, a twice continuously differentiable piecewise-smooth function is constructed to smooth the objective function of unconstrained support vector machine （SVM）, and it issues a piecewise-smooth support vector machine （PWESSVM）. Comparing to the other smooth approximation functions, the smooth precision has an obvious improvement. The theoretical analysis shows PWESSVM is globally convergent. Numerical results and comparisons demonstrate the classification performance of our algorithm is better than other competitive baselines.

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