svm经典论文

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.
3. V. D. Sanchez, Advanced support vector machines and kernel methods, Neurocomputing 2003, 55, 5-20.
4. C. Campbell, Kernel methods: a survey of current techniques, Neurocomputing 2002, 48, 63-84.
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.
6. J. A. K. Suykens, Support vector machines: A nonlinear modelling and control perspective, Eur. J. Control 2001, 7, 311-327.
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.
11. Kristin, P.B. and C. Colin, Support vector machines: hype or hallelujah? SIGKDD Explor. Newsl., 2000. 2(2): p. 1-13.
以上部分摘自：http://www.support-vector-machines.org/SVM_review.html

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