New Support Vector Algorithms

Authors:
Bernhard Schölkopf;Alex J. Smola;Robert C. Williamson;Peter L. Bartlett
Affiliations:
Microsoft Research, 1 Guildhall Street, Cambridge, U.K.;GMD FIRST, 12489 Berlin, Germany, and Department of Engineering, Australian National University, Canberra 0200, Australia;Department of Engineering, Australian National University, Canberra 0200, Australia;RSISE, Australian National University, Canberra 0200, Australia
Venue:
Neural Computation
Year:
2000

Citing 20
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Abstract

We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.