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Efficient SVM Regression Training with SMO (2001) [11 citations — 2 self]

by Gary William Flake ,  Steve Lawrence
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Abstract:

The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines

Citations

5044 Statistical Learning Theory – Vapnik - 1998
1240 A tutorial on support vector machines for pattern recognition – Burges - 1998
544 Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods – support vector learning – Platt - 1998
276 Detecting strange attractors in turbulence – Takens - 1981
188 ATutorial on Support Vector Regression – Smola, Scholkopf - 1998
174 An improved training algorithm for support vector machines – Osuna, Freund, et al. - 1997
136 Oscillations and chaos in physiological control systems – Mackey, Glass - 1977
111 Improvements to Platt’s SMO algorithm for SVM classifier design – Keerthi, Shevade, et al. - 2001
48 Nonlinear prediction of chaotic time series using support vector machines – Mukherjee, Osuna, et al. - 1997
33 Support vector machine --- reference manual – Saunders, Stitson, et al. - 1998
28 Using sparseness and analytic QP to speed training of Support Vector Machines – Platt - 1999
24 The Kernel-Adatron: a Fast and Simple Learning Procedure for Support Vector Machines – Frieß, Cristianini, et al. - 1998
6 Private communication – Platt - 1999
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