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基于样本分布不平衡的近似支持向量机 被引量：10

Proximal Support Vector Machines for Samples with Unbalanced Distribution
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摘要 针对标准的近似支持向量机(PSVM)没有考虑样本分布不平衡的问题,提出了一种新的PSVM算法—BPS-VM;根据训练样本数量的不平衡对正负样本集分别分配了不同的惩罚因子,并将原始优化问题中的惩罚因子由数值变更为一个对角阵,最后推导出了线性和非线性BPSVM的决策函数。实验结果表明,BPSVM的性能优于PSVM,与SVM方法相比效率更高。 For the problem of unbalanced data classification is not discussed in the standard Proximal Support Vector Machines, a new PSVM algorithm is presented, namely BPSVM. The different penalty factors are assigned to the positive and negative training sets according to the unbalanced population, and the penalty values are transformed into a diagonal rnatrim Finally the decision functions for the linear and nonlinear PSVM are obtained. The experimental results show that the generalization of BPSVM could be better than PSVM, and comparable to SVM with higher efficiency.
作者 陶晓燕 姬红兵 马志强
机构地区 西安电子科技大学电子工程学院 空军工程大学电讯工程学院
出处 《计算机科学》 CSCD 北大核心 2007年第5期174-176,共3页 Computer Science
基金 国防预研基金项目资助(No.51407030103DZ0117)
关键词 近似支持向量机 非平衡分布 平衡近似支持向量机 Proximal SVM （PSVM）, Unbalanced distribution, Balanced PSVM （BPSVM）
分类号 TP181 [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14

  • 1Vapnik V N.The nature of statistical learning theory.New York:Springer,2000
  • 2Cortes C,Vapnik V.Support vector networks.Machine learning,1995,20(1):273～297
  • 3Drucker H,Burges C J C,Kaufman L.Support vector regression machines.In:Advances in Neural Information Processing Systems,Cambridge:MIT Press,1997.155～161
  • 4许建华,张学工,李衍达.支持向量机的新发展[J].控制与决策,2004,19(5):481-484. 被引量：132
  • 5Platt J.Fast training of support vector machines using sequential minimal optimization.In:Advances in Kernel Methods-Support Vector Learning,Cambridge;MIT Press,1999.185～208
  • 6Suykens J A K,Vandewalle J.Least squares support vector machines.Neural processing letters,1999,9(3):293～300
  • 7Mangasarian O L.Generalized support vector machines.In:Advances in Large Margin Classifier.Cambridge:MIT Press,2000.135～146
  • 8Fung G,Mangasarian O L.Proximal Support Vector Machine Classifiers.Wisconsin:University of Wisconsin,2001
  • 9Agarwal D K.Shrinkage Estimator generalizations of proximal support vector machines.In:Proc.of the eighth ACM SIGKDD Int.Conf.on Knowledge Discovcry and Data Mining,Edmonton,Alberta,Canada:ACM Press,2002.173～182
  • 10Morik K,Brockhausen P,Joachims T.Combing statistical learning with a Knowledge-based approach:a case study in intensive care monitoring.In:Proc.16th Int.con on machine learning.San Mateo,Canada:Morgan Kaufman Publishers,1999.268～277

二级参考文献25

  • 1[1]Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers[A]. The 5th Annual ACM Workshop on COLT [C]. Pittsburgh:ACM Press, 1992. 144-152.
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  • 3[3]Drucker H, Burges C J C, Kaufman L, et al. Support vector regression machines [A]. Advances in Neural Information Processing Systems[C]. Cambridge: MIT Press, 1997. 155-161.
  • 4[4]Vapnik V N, Golowich S, Smola A. Support vector method for function approximation, regression estimation and signal processing [A]. Advances in Neural Information Processing Systems [ C ].Cambridge: MIT Press, 1997. 281-287.
  • 5[5]Vapnik V N. The Nature of Statistical Learning Theory[M]. New York: Springer-Verlag, 1995.
  • 6[6]Vapnik V N. Statistical Learning Theory [M]. New York: Wiley, 1998.
  • 7[7]Vapnik V N. The Nature of Statistical Learning Theory [M]. 2nd edition. New York: SpringerVerlag, 1999.
  • 8[8]Platt J. Fast training of support vector machines using sequential minimal optimization [ A ]. Advances in Kernel Methods - Support Vector Learning [C].Cambridge: MIT Press, 1999. 185-208.
  • 9[9]Suykens J A K, Vandewalle J. Least squares support vector machines [J]. Neural Processing Letters, 1999, 9(3): 293-300.
  • 10[10]Scholkopf B, Smola A J, Williamson R C, et al. New support vector algorithms [J]. Neural Computation,2000, 12(5) :1207-1245.

共引文献131

同被引文献155

引证文献10

二级引证文献145

计算机科学
2007年 第5期

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