English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

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

Schölkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., et al. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing, 45(11), 2758-2765. doi:10.1109/78.650102.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E9B4-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-E1FD-0
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Schölkopf, B1, 2, Author              
Sung , K, Author
Burges , C, Author
Girosi, F, Author
Niyogi, P, Author
Poggio, T, Author              
Vapnik, V, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 1997-11
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1109/78.650102
BibTex Citekey: 378
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE Transactions on Signal Processing
  Other : IEEE Trans. Signal Process.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 45 (11) Sequence Number: - Start / End Page: 2758 - 2765 Identifier: ISSN: 1053-587X
CoNE: https://pure.mpg.de/cone/journals/resource/954925594517