B. Schlkopf, and A.J. Smola, Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2001. Home/Search Document Not in Database Summary Related Articles Check |
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A Note on the Universal Approximation Capability of Support.. - Hammer, Gersmann (Correct)
....for appropriate xed constants and and functions f . f is often chosen as a sigmoidal function. However, note that not every function f yields a proper kernel; in particular, the popular choice f = tanh does not lead to a kernel. Conditions on f such that a kernel results have been presented in [20], for example. Gaussian kernels k g ( x; y) 7 exp(j x yj ) for xed . If used for regression, i.e. the training patterns ( x ; y i ) are contained in IR IR, the SVM implements the linear mapping x 7 w ( x) b : 6) Training can be formulated as the optimization ....
A. Smola, Support Vector Machines. Tutorial at ICANN'01, Vienna, Austria, 2001.
On Kernels, Margins, and Low-dimensional Mappings - Balcan, Blum, Vempala Self-citation (Kernels) (Correct)
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B. Scholkopf, A. J. Smola , \Learning with kernels. Support Vector Machines, Regularization, Optimization, and Beyond", MIT University Press, Cambridge, 2002.
Kernel Methods and Support Vector Machines - Schölkopf, Smola (2003) Self-citation (Scholkopf Smola) (Correct)
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B. Scholkopf and A. J. Smola. Support vector machines. In M. A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 1119--1125. MIT Press, 2nd edition, 2003.
On Kernels, Margins, and Low-dimensional Mappings - Balcan, Blum, Vempala Self-citation (Kernels) (Correct)
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B. Scholkopf, A. J. Smola , \Learning with kernels. Support Vector Machines, Regularization, Optimization, and Beyond ", MIT University Press, Cambridge, 2002.
Support Vector Neural Training - Duch (2004) (Correct)
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B. Schlkopf, and A.J. Smola, Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2001.
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