Adaptive constraint reduction for training support vector machines.

@inproceedings{Jung2008AdaptiveCR,
  title={Adaptive constraint reduction for training support vector machines.},
  author={Jin Hyuk Jung and Dianne P. O'Leary and Andr{\'e} L. Tits},
  year={2008}
}
A support vector machine (SVM) determines whether a given observed pattern lies in a particular class. The decision is based on prior training of the SVM on a set of patterns with known classification, and training is achieved by solving a convex quadratic programming problem. Since there are typically a large number of training patterns, this can be expensive. In this work, we propose an adaptive constraint reduction primal-dual interior-point method for training a linear SVM with `1 penalty… CONTINUE READING

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