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基于核变换的高性能支持向量机分类算法

DOI: 10.11834/jig.20081007

Keywords: 支持向量机核变换特征空间

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Abstract:

由于传统的支持向量机(SVM)算法的核函数没有考虑训练数据自身的特点,因而相对于具体的问题来说,往往不是最优的。为了获得最优的分类结果,提出了一种基于核变换思想的支持向量机分类方法。该方法首先根据训练样本的类属信息,通过对初始核进行线性变换来间接地达到改进输入空间到输出空间的映射函数的目的,同时利用变换后的核函数来求解分类数据特征空间的超平面方程。仿真和实验结果表明,采用此方法,不仅可以提高系统的分类性能和降低噪声的干扰,而且可以增强分类结果的鲁棒性。

References

[1]  Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions [J]. Neural Networks, 1999,12 (6) : 783-789.
[2]  Kwok James T, Tsang Ivor W. Learning with idealized kemels[ A ]. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) [C], Washington, DC, USA, 2003:1233-1237.
[3]  Xing E, Ng A, Jordan M, et al. Distance metric learning, with application to clustering with side-information [ A ]. In : Advances in Neural Information Processing System [C] , Cambridge, MA, USA, 2002 : 1 - 9.
[4]  Graf Arnulf B A, Smola Alexander J, Borer S. Classification in a normalized feature space using support vector machines [J]. IEEE Transactions on Neural Networks, 2003, 14(3 ) :597 - 605.
[5]  Lin Y, Lee Y, Wahba G. Support vector machines for classification in nonstandard situations [J]. Machine Learning, 2002, 46(3):191 -202.
[6]  Chiang J H, Hao P Y. A new kernel based fuzzy clustering approach : support vector clustering with cell growing [J]. IEEE Transactions on Fuzzy Systems, 2003, 11 ( 4 ) :518 - 527.
[7]  张翔 肖小玲 徐光佑.模糊支持向量机中隶属度的确定与分析[J].中国图象图形学报,2006,11(8):1188-1192.
[8]  Chen Y, Wang J Z. Support vector learning for fuzzy rule based classifications system [J]. IEEE Transactions on Fuzzy Systems, 2003, 11 ( 6 ) :716-728.
[9]  Cristianini N, Shawe-Tayor J, Elisseeff A, et al. On kernel target alignment [ A ]. In: Advances in Neural Information Processing System [C], Cambridge, MA, USA, 2002:367-373.
[10]  Herbrich R, Graepel T. A PAC-bayesian margin bound for linear classifiers:why SVM\\'s work [J]. IEEE Transactions on Information Theory, 2002:48( 12): 3140-3150.
[11]  Vapnik V, Chapelle O. Bounds on error expectation for support vector machines [J]. Neural Computation, 2000, 12 (9) :585 - 592.
[12]  Scholkopf B, Smola A. Learning with kernels: support vector machines, regularization, optimization, and beyond [M]. Cambridge, MA,USA: MIT Press, 2002:12-45.
[13]  Wisconsin breast cancer [EB/OL]. http://www. ics. uci. edu/- relearn/ MLRepository. html.

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