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支持向量机的研究进展 被引量：8

Research Progress of Support Vector Machine
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摘要 支持向量机是一种新的机器学习方法。对于支持向量机的算法、模型的选择及支持向量机的扩展进行了阐述及总结,并提出支持向量机的发展趋势和研究方向。 Support Vector Machine is a new kind of machine learning method. Reviews recent development of SVM in following major aspects： algorithm, model selection and expanding SVM, points out the future research directions of SVM.
作者 陈俏 曹根牛 谢丽娟
机构地区 西安科技大学理学院
出处 《现代计算机》 2009年第4期47-50,54,共5页 Modern Computer
基金 陕西省教育厅专项科研计划项目(No.07JK312)
关键词 支持向量机 二次规划 特征空间 核函数 Support Vector Machine（SVM） Quadratic Programming Character Space Kernel Function
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33

  • 1Cortes C, Vapnik V. Support Vector Networks[J]. Machine Learning, 1995, 20:273-297
  • 2Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machinesand Other Kemel-Based Learning Methods [M]. Cambridge: Cambridge University Press, 2000
  • 3Vapnik V. The Nature of Statiscal Learning Theory[M]. New York: Springer, 1995
  • 4Osuna E, Freund R, Girosi F. An Improved Training Algorithm for Support Vector Machines[C]. Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing Ⅶ. Amelea Island, 1997:276-285
  • 5Chih-Jen Lin, et al. On the Convergence of the Decomposition Method for Support Vector Machines[J]. IEEE Transactions on Neural Networks, 2001(12), 6:1288-1298
  • 6Osuna E, Freund R, Girosi F. An Improved Training Algorithm for Support Vector Machines[C]. Proceedings of the 1997 IEEE Workshop on Neural Network for Signal Processing Ⅶ, Amelea Island, 1997:276-285
  • 7Joachims T. Making Large-Scale Support Vector Machine Learning Practical[M]. In: Scholkopf B Burges C, Smola A, ed. Advances in Kernel Methods-Support Vector Learning, Cambridge,MA: MITPress, 1999:169-184
  • 8LIN Chihjen. On the Convergence of the Decomposition Method for Support Vector Machines[J]. IEEE Transactions on Neural Networks, 2001, 12(6): 1288-1298
  • 9Platt J. Fast Training of Support Vector Machines Using Sequential Minimal Optimization[M]. In: Scholkopf B, Burges C, Smola A, ed. Advances in Kernel Methods-Support Vector Learning[J], Cambridge, MA: MITPress, 1999: 185- 208
  • 10Cauwenberghs G, Poggio T. Incremental and Decremental Support Vector Machine Learning[M]. In: Advances in Neural Information Processing Systems (NIPS 2000),Cambridge, MA: MIT Press, 2001

二级参考文献21

  • 1[2]LIN Chun-fu, WANG Sheng-de. Fuzzy support vector machines [J]. IEEE Trans on Neural Networks, 2002,13(2) :464-471.
  • 2[3]TOY F E H, CAO L J. Descending support vector machines for financial time series forecasting [J]. Neural Processing Letters, 2002, 15(2): 179-195.
  • 3[4]TOY F E H, CAO L J. Modified support vector machines in financial time series forecasting [J]. Neurocomputing, 2002, 48: 847- 861.
  • 4[5]VAPNIK V N. The nature of statistical learning [M].Berlin: Springer, 1995.
  • 5[6]FLAKE G W, LAWRENCE S. Efficient SVM regression training with SMO [J]. Machine Learning, 2002,41(1): 271-290.
  • 6[7]TAY F E H, CAO L. Application of support vector machines in financial time series forecasting [J]. Omega The International Journal of Management Science,2001, 29(4):309-317.
  • 7Ingo Steinwart, On the influence of the kernel on the generalization ability of support vector machines. Department of mathematics and computer science, Friedrich Schiller University(Jena): Technical Report TR-01-01, 2001 (Available as http://www. minet. uni-jena. de /Math-Net /reports/rep-com.html).
  • 8Shun-ichi Amari, Si Wu. Improving support vector machine classifiers by modifying kernel functions. Neural Networks,1999, 12:783-789.
  • 9Vapnik V. The Nature of Statistical Learning Theory. New York:Verlag, 1995.
  • 10Scholkpf B. Support vector learning[Ph D dissertation]. Berlin University, Berlin, 1997.

共引文献168

同被引文献82

引证文献8

二级引证文献32

现代计算机
2009年 第4期

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