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总间隔模糊超球学习机

, PP. 237-247

Keywords: 总间隔,支持向量,模糊支持向量机,超球体

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Abstract:

为解决传统支持向量机易出现学习“过拟合”和丢失数据统计特征等问题,通过引入模糊隶属度和总间隔思想,提出一种基于总间隔的最大间隔最小包含模糊球形学习机(TMF-SSLM),使得一类(正类)被包含于一个最小包含超球内,而另一类(负类)与该超球间隔最大化,从而同时实现类间间隔的增大和正负两类类内体积的缩小。通过使用差异成本,解决不平衡训练样本问题。引入总间隔和模糊性惩罚,克服传统软间隔分类机的过拟合问题,显著提升球形学习机的泛化能力。采用UCI实际数据集分别对二类和一类模式分类进行实验,结果显示TMF-SSLM具有优于相关方法的稳定分类性能。

References

[1]  Wen Chuanjun,Zhan Yongzhao,Chen Changjun.Maximal-Margin Minimal-Volume Hypersphere Support Vector Machine.Control and Decision,2010,25(1): 79-83 (in Chinese)(文传军,詹永照,陈长军.最大间隔最小体积球形支持向量机.控制与决策,2010,25(1): 79-83)
[2]  Chung F L,Wang Shitong,Deng Zhaohong,et al.Fuzzy Kernel Hyperball Perceptron.Applied Soft Computing,2004,5(1): 67-74
[3]  Wu Mingrui,Ye Jieping.A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(11): 2088-2092
[4]  Schlkopf B,Smola A J,Williamson R,et al.New Support Vector Algorithms.Neural Computation,2000,12(5): 1207-1245
[5]  Deng Zhaohong,Chung F L,Wang Shitong.FRSDE: Fast Reduced Set Density Estimator Using Minimal Enclosing Ball Approximation.Pattern Recognition,2008,41(1): 1363-1372
[6]  Peng Xinjun,Wang Yifei.Total Margin v-Support Vector Machine and Its Geometric Problem.Pattern Recognition and Artificial Intelligence,2009,22(1): 8-16 (in Chinese)(彭新俊,王翼飞.总间隔v-支持向量机及其几何问题.模式识别与人工智能,2009,22(1): 8-16)
[7]  Lin C F,Wang S D.Fuzzy Support Vector Machines.IEEE Trans on Neural Network,2002,13(2): 464-471
[8]  Liu Y H,Chen Y T.Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines.IEEE Trans on Neural Networks,2007,18(1): 178-192
[9]  Yoon M,Yun Y,Nakayama H.A Role of Total Margin in Support Vector Machines // Proc of the International Joint Conference on Neural Network.Atlanta,USA,2003,III: 2049-2053
[10]  Yoo M,Yun Y,Nakayama H.Total Margin Algorithms in Support Vector Machines.IEICE Trans on Information and Systems,2004,87(5): 1223-1230
[11]  Liu Y H,Chen Y T.Total Margin Based Adaptive Fuzzy Support Vector Machines for Multi-View Face Recognition // Proc of the IEEE International Conference on Systems,Man and Cybernetics.Hawaii,USA,2005,II: 1704-1711
[12]  Peng Xinjun,Wang Yifei.Geometric Algorithms to Large Margin Classifier Based on Affine Hulls.IEEE Trans on Neural Networks and Learning Systems,2012,23(2): 236-246
[13]  Tax D M J,Duin R P W.Support Vector Data Description.Machine Learning,2004,54(1): 45-66
[14]  Chung F L,Deng Zhaohong,Wang Shitong.From Minimum Enclosing Ball to Fast Fuzzy Inference System Training on Large Datasets.IEEE Trans on Fuzzy System,2009,17(1): 173-184
[15]  Wang J G,Neskovic P,Cooper L N.Pattern Classification via Single Sphere // Proc of the 8th International Conference on Discovery Science.Singapore,Singapore,2005: 241-252
[16]  Hao P Y,Chiang J H,Lin Y H.A New Maximal Margin Spherical-Structured Multi-Class Support Vector Machine.Applied Intelligence,2009,30(2): 98-111
[17]  Liu Y,Zheng Y F.Minimum Enclosing and Maximum Excluding Machine for Pattern Description and Discrimination // Proc of the 18th International Conference on Pattern Recognition.Hong Kong,China,2006,III: 129-132
[18]  Ge Lei,Wu Huizhong.A Kernel-Based Fuzzy Greedy Multiple Hyperspheres Covering Algorithm for Pattern Classification.Neurocomputing,2008,72(1/2/3): 313-320.
[19]  Burges C J C.A Tutorial on Support Vector Machines for Pattern Recognition.Data Mining and Knowledge Discovery,1998,2(2): 955-974
[20]  Vapnik V,Chapelle O.Bounds on Error Expectation for Support Vector Machines.Neural Computation,2000,12(1): 2013-2036

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