| SVM - Support Vector Machines |
- Review: Applications of Support Vector Machines in Chemistry, Rev. Comput. Chem. 2007, 23, 291-400
- V. Vapnik and A. Chervonenkis, Theory of Pattern Recognition, Nauka, Moscow, 1974.
- V. Vapnik, Estimation of Dependencies Based on Empirical Data, Nauka, Moscow, 1979.
- V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.
- V. Vapnik, Statistical Learning Theory, Wiley-Interscience, New York, 1998.
- B. Schölkopf, C. J. C. Burges, and A. J. Smola, Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, 1999.
- N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
- A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, 2000.
- V. Kecman, Learning and Soft Computing, MIT Press, Cambridge, MA, 2001.
- B. Schölkopf and A. J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002.
- T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms, Kluwer, 2002.
- R. Herbrich, Learning Kernel Classifiers, MIT Press, Cambridge, MA, 2002.
- J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002.
- J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, 2004.
|
