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| SVM - Support Vector Machines |
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- Review: Applications of Support Vector Machines in Chemistry, Rev. Comput. Chem. 2007, 23, 291-400
- 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.
- A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Stat. Comput. 2004, 14, 199-222.
- V. D. Sanchez, Advanced support vector machines and kernel methods, Neurocomputing 2003, 55, 5-20.
- C. Campbell, Kernel methods: a survey of current techniques, Neurocomputing 2002, 48, 63-84.
- 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.
- J. A. K. Suykens, Support vector machines: A nonlinear modelling and control perspective, Eur. J. Control 2001, 7, 311-327.
- V. N. Vapnik, An overview of statistical learning theory, IEEE Trans. Neural Netw. 1999, 10, 988-999.
- 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.
- C. J. C. Burges, A tutorial on Support Vector Machines for pattern recognition, Data Min. Knowl. Discov. 1998, 2, 121-167.
- 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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