Fast kernel SVM training via support vector identification

  title={Fast kernel SVM training via support vector identification},
  author={Xue Mao and Zhouyu Fu and Ou Wu and Weiming Hu},
  journal={2016 23rd International Conference on Pattern Recognition (ICPR)},
Training kernel SVM on large datasets suffers from high computational complexity and requires a large amount of memory. However, a desirable property of SVM is that its decision function is solely determined by the support vectors, a subset of training examples with non-vanishing weights. This motivates a novel efficient algorithm for training kernel SVM via support vector identification. The efficient training algorithm involves two steps. In the first step, we randomly sample the training… CONTINUE READING


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