Neighborhood based sample and feature selection for SVM classification learning

@article{He2011NeighborhoodBS,
  title={Neighborhood based sample and feature selection for SVM classification learning},
  author={Qiang He and Zongxia Xie and Qinghua Hu and Congxin Wu},
  journal={Neurocomputing},
  year={2011},
  volume={74},
  pages={1585-1594}
}
Support vector machines (SVMs) are a class of popular classification algorithms for their high generalization ability. However, it is time-consuming to train SVMs with a large set of learning samples. Improving learning efficiency is one of most important research tasks on SVMs. It is known that although there are many candidate training samples in some learning tasks, only the samples near planes. Finding these samples and training SVMs with them will greatly decrease training time and space… CONTINUE READING
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