Max-Margin Stacking and Sparse Regularization for Linear Classifier Combination and Selection

@article{Sen2011MaxMarginSA,
  title={Max-Margin Stacking and Sparse Regularization for Linear Classifier Combination and Selection},
  author={Mehmet Umut Sen and Hakan Erdogan},
  journal={CoRR},
  year={2011},
  volume={abs/1106.1684}
}
The main principle of stacked generalization (or Stacking) is using a second-level generalizer to combine the outputs of base classifiers in an ensemble. In this paper, we investigate different combination types under the stacking framework; namely weighted sum (WS), class-dependent weighted sum (CWS) and linear stacked generalization (LSG). For learning the weights, we propose using regularized empirical risk minimization with the hinge loss. In addition, we propose using group sparsity for… CONTINUE READING

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