Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
Scholkopf, B.
Kah-Kay Sung
Burges, C.J.C.
Girosi, F.
Niyogi, P.
Poggio, T.
Vapnik, V.
Max-Planck-Inst. fur Biol. Kybernetik, Tubingen ;
This paper appears in: Signal Processing, IEEE Transactions on
Publication Date: Nov 1997
Volume: 45,
Issue: 11
On page(s): 2758-2765
ISSN: 1053-587X
References Cited: 27
CODEN: ITPRED
INSPEC Accession Number: 5765017
Digital Object Identifier: 10.1109/78.650102
Current Version Published: 2002-08-06
Abstract
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 determined by X-means clustering, and the weights
are computed using error backpropagation. We consider three machines,
namely, a classical RBF machine, an SV machine with Gaussian kernel, and
a hybrid system with the centers determined by the SV method and the
weights trained by error backpropagation. Our results show that on the
United States postal service database of handwritten digits, the SV
machine achieves the highest recognition accuracy, followed by the
hybrid system. The SV approach is thus not only theoretically
well-founded but also superior in a practical application
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