SVM经典论文,如资源描述所示

上传者: 45700531 | 上传时间: 2021-04-06 09:11:14 | 文件大小: 2.63MB | 文件类型: ZIP
1. P. H. Chen, C. J. Lin, and B. Schölkopf, A tutorial on ν-support vector machines, Appl. Stoch. Models. Bus. Ind. 2005, 21, 111-136. 2. A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Stat. Comput. 2004, 14, 199-222. 5. K. R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, An introduction to kernel-based learning algorithms, IEEE Trans. Neural Netw. 2001, 12, 181-201. 7. V. N. Vapnik, An overview of statistical learning theory, IEEE Trans. Neural Netw. 1999, 10, 988-999. 8. B. Schölkopf, S. Mika, C. J. C. Burges, P. Knirsch, K. R. Muller, G. Ratsch, and A. J. Smola, Input space versus feature space in kernel-based methods, IEEE Trans. Neural Netw. 1999, 10, 1000-1017. 9. C. J. C. Burges, A tutorial on Support Vector Machines for pattern recognition, Data Min. Knowl. Discov. 1998, 2, 121-167. 10. A. J. Smola and B. Schölkopf, On a kernel-based method for pattern recognition, regression, approximation, and operator inversion, Algorithmica 1998, 22, 211-231.

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