Nonlinear Classification via Linear SVMs and Multi-Task Learning

@inproceedings{Mao2014NonlinearCV,
  title={Nonlinear Classification via Linear SVMs and Multi-Task Learning},
  author={Xue Mao and Ou Wu and Weiming Hu and Peter O'Donovan},
  booktitle={CIKM},
  year={2014}
}
Kernel SVM is prohibitively expensive when dealing with large nonlinear data. While ensembles of linear classifiers have been proposed to address this inefficiency, these methods are time-consuming or lack robustness. We propose an efficient classifier for nonlinear data using a new iterative learning algorithm, which partitions the data into clusters, and then trains a linear SVM for each cluster. These two steps are combined into a graphical model, with the parameters estimated efficiently… CONTINUE READING
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SHOWING 1-4 OF 4 REFERENCES

Mixing Linear SVMs for Nonlinear Classification

  • IEEE Transactions on Neural Networks
  • 2010
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Clustered Support Vector Machines

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL