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用球结构的支持向量机解决多分类问题 被引量：48

Solving the Problem of Multi-class Pattern Recognition with Sphere-structured Support Vector Machines
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摘要 　支持向量机是从统计学习理论中导出的,从研究二分类开始,发展至今,虽然提出了很多多类别分类的相关算法,但都各有不足之处.提出基于球结构的支持向量算法,适用于规模比较庞大的多类别问题,并对其性质进行了讨论. Support vector machines (SVMs) are learning algorithms derived from statistical learning theory. The SVMs approach was originally developed to solve binary classification problems. There are some methods to solve multi_class classification problems, such as one_against_rest, one_against_one, all_together and so on. But the computing time of all these methods are too long to solve large_scale problems. In this paper SVMs architectures for multi_class problems are discussed. Furthermore we provide a new algorithm called sphere_structured SVMs to solve the multi_class problem. We show the algorithm in detail and analyze its characteristics. Not only is the number of convex quadratic programming problems in sphere_structured SVMs small, but also the number of variables in each programming is the least. The computing time of classification is reduced. Otherwise, the characteristics of sphere_structured SVMs make it easy for data to expand.
作者 朱美琳 刘向东 陈世福
机构地区 南京大学计算机软件新技术国家重点实验室
出处 《南京大学学报（自然科学版）》 CAS CSCD 北大核心 2003年第2期153-158,共6页 Journal of Nanjing University（Natural Science）
关键词 球结构 多分类问题 支持向量机 核函数 球分类 模式识别 统计学习理论 SVM, kernel function, sphere-classifier, pattern recognition
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献12

  • 1Vapnik V. The nature of statistical learning theory. New York: Springer-Verlag, 1995, 5-13.
  • 2Burges C J C, Scholkopf B. Improving the accuracy and speed of support vector learning machines.Advances in Neural Information Processing Systems 9. Cambridge: MIT Press, 1997: 375-381.
  • 3Blanz V, Scholkopf B, Bultho H, et al. Comparison of view-based object recognition algorithms usingrealistic 3D models. Artificial Neural Networks - ICANN'96. Berlin: Springer Lecture Notes in Computer Science, 1996: 251-256.
  • 4Joachims T. Text categorization with support vector machines: Learning with many relevant features.Proceedings of the European Conference on Machine Learning. Berlin: Springer, 1998:137-142.
  • 5Drucker H, Wu D, Vapnik V. Support vector machines for span categorization. IEEE. Transactions on Neural Networks, 1999, 10(5): 1 048-1 054.
  • 6Muller K R, Smola A J , Ratsch G, et al. Predicting time series with support vector machines. Artificial Neural Networks - ICANN'97. Berlin: Springer Lecture Notes in Computer Science, 1997:999-1 004.
  • 7Brown M P S, Grundy W N, Lin D, et al. Knowledge-based analysis of microarray gene expression data using support vector machines. Proceedings of the National Academy of Sciences, 2000, 97( 1): 262-267.
  • 8Kreβel U. Pairwise classification and support vector machines. Advances in Kernel Methods. Cambridge:MIT Press, 1999, 255-268.
  • 9Guo G, Li S, Chan K. Face recognition by support vector machines. Proceedings of the International Conferences on Automatic Face and Gesture Recognition. 2000: 196-201.
  • 10Mattera D, Haykin S. Support vector machines for dynamic reconstruction of a chaotic system, Advances in Kernel Methods - Support Vector Learning. Cambridge: MIT Press, 1999: 211-242.

同被引文献503

引证文献48

二级引证文献297

南京大学学报（自然科学版）
2003年 第2期

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