提出了一种新的支持向量机分类器的设计方法.该方法利用主成分分析(PCA)及聚类技术在原问题空间中求解,减少了支持向量机分类器中支持向量的维数,且将原问题空间与特征空间中的问题归结为同一类的设计问题.
[1] 李凯, 郭子雪, Li Kai, Guo Zi-xue. 基于SVM的函数模拟 [J]. 河北大学学报(自然科学版) 2001.doi:10.3969/j.issn.1000-1565.2001.01.003
[2] PLATT C J. Sequential minimal optimization for SVM [OL]. http://www.ics.uci.edu/xge/svm/smo.html 2001.
[3] Keerthi SS., Bhattacharyya C., Murthy KRK., Shevade SK.. A fast iterative nearest point algorithm for support vector machine classifier design [J]. IEEE Transactions on Neural Networks 2000, 1(1).
[4] Scholkopf B, SMOLA A, MULLER K R. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural Computation 1998.
[5] Vapnik V. The Nature of Statistical Learning Theory [M]. New York: Springer-Verlag Press 1995.
[6] Chih-Chung Chang, Chih-Jen Lin. Training ν-Support Vector Classifiers: Theory and Algorithms [J]. Neural computation 2001, 9(9).
[7] BERNHARD SCHLOPF, ALEX J SMOLA. New suport vector algorithms [J]. Neural Computation 2000, 12.