Comparing support vector machines with Gaussian kernels to radial basis function classifiers

@article{Schlkopf1997ComparingSV,
  title={Comparing support vector machines with Gaussian kernels to radial basis function classifiers},
  author={Bernhard Sch{\"o}lkopf and Kah Kay Sung and Christopher J. C. Burges and Federico Girosi and Partha Niyogi and Tomaso A. Poggio and Vladimir Vapnik},
  journal={IEEE Trans. Signal Processing},
  year={1997},
  volume={45},
  pages={2758-2765}
}
The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are… CONTINUE READING
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