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Vishwanathan, S.V.N.; Smola, Alexander; Murty, M. Narasimha


We present a fast iterative support vector training algorithm for a large variety of different formulations. It works by incrementally changing a candidate support vector set using a greedy approach, until the supporting hyperplane is found within a finite number of iterations. It is derived from a simple active set method which sweeps through the set of Lagrange multipliers and keeps optimality in the unconstrained variables, while discarding large amounts of bound-constrained variables. The...[Show more]

CollectionsANU Research Publications
Date published: 2003
Type: Conference paper
Source: Proceedings of the Twentieth International Conference on Machine Learning


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