Mixing Linear SVMs for Nonlinear Classification

@article{Fu2010MixingLS,
  title={Mixing Linear SVMs for Nonlinear Classification},
  author={Zhouyu Fu and Antonio Robles-Kelly and Jun Zhou},
  journal={IEEE Transactions on Neural Networks},
  year={2010},
  volume={21},
  pages={1963-1975}
}
In this paper, we address the problem of combining linear support vector machines (SVMs) for classification of large-scale nonlinear datasets. The motivation is to exploit both the efficiency of linear SVMs (LSVMs) in learning and prediction and the power of nonlinear SVMs in classification. To this end, we develop a LSVM mixture model that exploits a divide-and-conquer strategy by partitioning the feature space into subregions of linearly separable datapoints and learning a LSVM for each of… CONTINUE READING
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On linear SVM mixtures for nonlinear classification

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