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基于Hausdorff距离的支持向量机训练集选取方法

Training Set Selection Method for Support Vector Machine Based Hausdorff Distance
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摘要 支持向量机是一种新的数据分析方法,应用到越来越多学习问题领域。支持向量机的训练速度和精度与训练集的选取有很大的关系,在对比分析分解算法、SMO(序列最小优化)算法和增量算法的特点和不足的基础上,提出了一种基于Hausdorff距离的训练集的选取方法,利用Libsvm在几个标准数据库上对几种算法进行实验,结果表明,新的算法在速度和精度上具有较大的提高。 Support vector machines is a new method to analyze data and is popular in many learning fields. Its training speed and accuracy depend mainly on the training set selection method. By comparing the characteristic and disadvantage of Chunking, SMO and incremental learning algorithm, a new algorithm is proposed which is based on the Hausdorff distance. Comparing the new algorithm with other methods on some standard databases with the help of Libsvm, and drawing conclusion that the new algorithm can get improvements on accuracy.
作者 彭四海
机构地区 中国空空导弹研究院
出处 《航空兵器》 2007年第5期45-48,共4页 Aero Weaponry
关键词 支持向量机 HAUSDORFF距离 分解算法 SMO LIBSVM 增量算法 support vector machines Hausdorff distance Chunking SMO Libsvm incremental algorithm
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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  • 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.
  • 2[2]Cortes C, Vapnik V N. Support vector networks[J].Machine Learning, 1995, 20(3): 273-297.
  • 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.

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航空兵器
2007年 第5期

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