南京大学学报(自然科学版) ›› 2013, Vol. 49 ›› Issue (5): 637643.
包文颖1,胡清华2,王长忠1**
Bao Wen-Ying1, Hu Qing-Hua2, Wang Chang-Zhong1
摘要: support vector machine based on multi-granulations, MG-SVM)。首先,利用多粒度理论对数据进行粒化与压缩;然后,对压缩后的数据利用支持向量机寻找最优超平面并进行分类;最后利用UCI中一些标准数据进行数据压缩与分类试验。与传统的支持向量机分类方法相比,MG-SVM算法在保持或提高经典支持向量机的分类和泛化能力的同时,有效地降低了时间复杂度。
[1] Corts C, Vapnik V N. Support vector networks. Machine Learning, 1995, 20: 273~297. [2] Burges C. A turtorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121~167. [3] Cortes C, Vapnik V N. Support vector networks. Machine Learning, 1995, 20: 273~297. [4] Vapnik V N. The nature of statistical learning theory. New York: Springer-Verlag, 1995, 314. [5] Scholkopf B, Burges C, Smola A. Advances in kernel methods: Support vector learning. Cambridge, MA: MIT Press, 1999, 314. [6] Wang L P, Fu X. Data mining with computational intelligence. Berlin: Springer, 2005, 356. [7] Wang L P. Support vector machines: Theory and applications. Berlin: Springer, 2005, 431. [8] Bai J W, Wang W J, Guo H S. A novel support vector machine active learning strategy. Journal of Nanjing University (Natural Sciences), 2012, 48(2): 182~189.( 白龙飞 , 王文剑 , 郭虎升 . 一种新的支持向量机主动学习策略 . 南京大学学报 ( 自然科学 ),2012,48(2):182~189) [9] Burges C J C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2: 121~167. [10] Osuna E, Frenud R, Girosi F. An improved training algorithm for support vector machines. Proceedings of IEEE Workshop on Neural Networks for Signal Processing. New York, USA, 1997: 276~285. [11] Tang Y C, Jin B, Zhang Y Q. Granular support vector machines for medical binary classification problems. Fogl G B. Proceedings of the IEEE CIBIB. Piscataway, IEEE Computational Intelligence Society, 2004: 73~78. [12] Cortes C, Vapnik V. Support vector networks. Machine Learning, 1995, 20: 273~297. [13] Shin H, Cho S. Fast pattern selection for support vector classifiers. Lecture Notes in Artificial Intelligence, 2003, 2637: 376~387. [14] Shin H, Cho S. Invariance of neighborhood relation under input space to feature space mapping. Pattern Recognition Letters, 2005, 26: 707~718. [15] Boser B, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers. Proceedings of the 5th Annual ACM Work shop on Computational Learning Theory. New York: ACM Press, 1992: 144~152. [16] Lee Y J, Mangasarian L. RSVM: Reduced support vector machines. Proceedings of the 1st SIAM International Conference on Data Mining, 2001:5~7. [17] Zheng S, Lu X, Zheng N, et al. Unsupervised clustering based reduced support vector machines. Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing, 2003: 821~824. [18] Hu Q H, Yu D R, Xie Z X. Selecting samples and features for SVM based on neighborhood model. The 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Toronto, 2007:508~517. [19] Shin H, Cho S. Fast pattern selection for support vector classifiers. Lecture Notes in Artificial Intelligence, 2003, 2637: 376~387. [20] Hu Q H, Yu D R, Liu J F, et al. Neighborhood rough set based heterogeneous feature subset selection. Information Sciences, 2008, 178: 3577~3594. |
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