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New Support Vector Algorithms

GMD FIRST, 12489 Berlin, Germany, and Department of Engineering, Australian National University, Canberra 0200, Australia

Alex J. Smola

GMD FIRST, 12489 Berlin, Germany, and Department of Engineering, Australian National University, Canberra 0200, Australia

Robert C. Williamson

Department of Engineering, Australian National University, Canberra 0200, Australia

Peter L. Bartlett

RSISE, Australian National University, Canberra 0200, Australia.

Correspondence: Present address: Microsoft Research, 1 Guildhall Street, Cambridge, U.K.

We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter 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 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 , and report experimental results.

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