Corpus ID: 10733972

Fast Prediction with SVM Models Containing RBF Kernels

@article{Claesen2014FastPW,
  title={Fast Prediction with SVM Models Containing RBF Kernels},
  author={Marc Claesen and Frank De Smet and Johan A. K. Suykens and Bart De Moor},
  journal={ArXiv},
  year={2014},
  volume={abs/1403.0736}
}
  • Marc Claesen, Frank De Smet, +1 author Bart De Moor
  • Published in ArXiv 2014
  • Mathematics, Computer Science
  • We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner products between support vectors and test instances. The approximation is applicable to all kernel methods featuring sums of kernel evaluations and makes no assumptions regarding data normalization. The prediction speed of approximated models no longer relates to the amount of support vectors but is quadratic in terms of the… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 38 REFERENCES

    Efficient Classification for Additive Kernel SVMs

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    LIBSVM: A library for support vector machines

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Fast training of Support Vector Machines for regression

    • Davide Anguita, Andrea Boni, Stefano Pace
    • Computer Science
    • Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
    • 2000
    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL

    An effective method of pruning support

    • X. Liang
    • 2010
    VIEW 2 EXCERPTS

    An effective method of pruning support vector machine classifiers

    • X. Liang
    • Neural Networks , IEEE Transactions on
    • 2010
    VIEW 1 EXCERPT

    Efficient additive kernels via explicit feature maps

    VIEW 1 EXCERPT