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支持向量机在分类中的应用 被引量：23

Applications of Support Vector Machines in Classification
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摘要 通过引入结构风险最小化原则和最优分类面的概念,介绍了支持向量机及其用于非线性分类的基本原理和训练算法,并选用不同的核函数及参数对一组线性不可分的两类样本进行了划分识别,得到了较好的效果,并对结果进行了分析说明,展望了支持向量机的发展趋势。 This paper introduces the theory and training algorithm of the support vector machine which is applied in nonlinear classification and recognition by the way of bringing in the concept such as structural risk minimization principle and optimal hyperplane, then a set of nonlinear binary samples are successfully classified by using different kernel functions, followed by discussion to the results. After that current multi-class classification algorithms and application areas are reviewed. Finally future developments are prospected.
作者 陆波 尉询楷 毕笃彦
机构地区 空军工程大学工程学院
出处 《中国图象图形学报》 CSCD 北大核心 2005年第8期1029-1035,共7页 Journal of Image and Graphics
关键词 支持向量机 最优分类面 非线性分类 二次规划 support vector machine, optimal hyperplane, nonlinear classification, quadratic programming
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献21

  • 1Inoue T, Abe S. Fuzzy Support Vector Machines for Pattern Classication [R]. TR2001-005, WashingtonDC, America:Washington Istitute of Technology.
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  • 5尉询楷,陆波,汪诚,路建明,李应红.支持向量机在航空发动机故障诊断中的应用[J].航空动力学报,2004,19(6):844-848. 被引量：29
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二级参考文献16

  • 1[1]V Vapnik. The Nature of Statistical Learning Theory[M]. New York: Springer-Verlag, 1995.
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共引文献42

同被引文献208

引证文献23

二级引证文献114

中国图象图形学报
2005年 第8期

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