Header logo is

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

1997

Article

ei


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.

Author(s): Schölkopf, B. and Sung, K. and Burges, C. and Girosi, F. and Niyogi, P. and Poggio, T. and Vapnik, V.
Journal: IEEE Transactions on Signal Processing
Volume: 45
Number (issue): 11
Pages: 2758-2765
Year: 1997
Month: November
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/78.650102
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@article{378,
  title = {Comparing support vector machines with Gaussian kernels to radial basis function classifiers },
  author = {Sch{\"o}lkopf, B. and Sung, K. and Burges, C. and Girosi, F. and Niyogi, P. and Poggio, T. and Vapnik, V.},
  journal = {IEEE Transactions on Signal Processing},
  volume = {45},
  number = {11},
  pages = {2758-2765},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = nov,
  year = {1997},
  month_numeric = {11}
}