Core Vector Machines: Fast SVM Training on Very Large Data Sets

@article{Tsang2005CoreVM,
  title={Core Vector Machines: Fast SVM Training on Very Large Data Sets},
  author={Ivor W. Tsang and James T. Kwok and Pak-Ming Cheung},
  journal={Journal of Machine Learning Research},
  year={2005},
  volume={6},
  pages={363-392}
}
Standard SVM training has O(m3) time andO(m2) space complexities, where m is the training set size. It is thus computationally infeasible on very larg e data sets. By observing that practical SVM implementations onlyapproximatethe optimal solution by an iterative strategy, we scale up kernel methods by exploiting such “approximateness” in t h s paper. We first show that many kernel methods can be equivalently formulated as minimum en closing ball (MEB) problems in computational geometry. Then… CONTINUE READING
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