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]
|Collections||ANU Research Publications|
|Source:||Proceedings of the Twentieth International Conference on Machine Learning|
|01_Vishwanathan_SimpleSVM_2003.pdf||404.6 kB||Adobe PDF||Request a copy|
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