PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Training Support Vector Machines with Multiple Equality Constraints
Wolf Kienzle and Bernhard Schölkopf
In: ECML 2005, 3-7 Oct 2005, Porto, Portugal.


In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1188
Deposited By:Wolf Kienzle
Deposited On:21 November 2005