a fuzzy support vector machine algorithm with dual membership based on hypersphere

(1) School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China; (2) Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China; (3) Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China

[1]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[2]  Jian Xu,et al.  Neighborhood Preprocessing SVM for Large-Scale Data Sets Classification , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[3]  Xuehua Li,et al.  Fuzzy Theory Based Support Vector Machine Classifier , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[4]  Vasile Palade,et al.  FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning , 2010, IEEE Transactions on Fuzzy Systems.

[5]  Pei-Yi Hao,et al.  A New Support Vector Machine with Fuzzy Hyper-Plane and Its Application to Evaluate Credit Risk , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[6]  Qiang He,et al.  Multi-class fuzzy support vector machine based on dismissing margin , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[7]  Liu San.  Fuzzy Support Vector Machine Method Based on Border Vector Extraction , 2008 .

[8]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[9]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[10]  Tshilidzi Marwala,et al.  Genetic Algorithms, Neural Networks, Fuzzy Inference System, Support Vector Machines for Call Performance Classification , 2009, 2009 International Conference on Machine Learning and Applications.

[11]  Liu San-yang.  Fuzzy support vector machine based on cluster hyperplane , 2008 .

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.