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

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by Gary William Flake , Steve Lawrence

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@MISC{Flake01efficientsvm,
    author = {Gary William Flake and Steve Lawrence},
    title = {Efficient SVM Regression Training with SMO},
    year = {2001}
}

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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

5124	The Nature of Statistical Learning Theory – Vapnik - 1995
1264	A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 – Burges - 1998
558	Fast training of support vector machines using sequential minimal optimization – Platt - 1998
277	Detecting strange attractors in turbulence – Takens - 1981
193	A Tutorial on Support Vector Regression – Smola, Scholkopf
176	Improved training algorithm for support vector machine – Osuna, Freund, et al. - 1997
138	Oscillation and chaos in physiological control systems – Mackey, Glass - 1977
112	Improvements to Platt's SMO algorithm for SVM classifier design – Keerthi, Shevade, et al. - 1999
51	Nonlinear prediction of chaotic time series using support vector machines – Girosi, Mukherjee, 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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