@article{2180,
  abstract     = {Weighted majority votes allow one to combine the output of several classifiers or voters. MinCq is a recent algorithm for optimizing the weight of each voter based on the minimization of a theoretical bound over the risk of the vote with elegant PAC-Bayesian generalization guarantees. However, while it has demonstrated good performance when combining weak classifiers, MinCq cannot make use of the useful a priori knowledge that one may have when using a mixture of weak and strong voters. In this paper, we propose P-MinCq, an extension of MinCq that can incorporate such knowledge in the form of a  constraint over the distribution of the weights, along with general proofs of convergence that stand in the sample compression setting for data-dependent voters. The approach is applied to a vote of k-NN classifiers with a specific modeling of the voters' performance. P-MinCq significantly outperforms the classic k-NN classifier, a symmetric NN and MinCq using the same voters. We show that it is also competitive with LMNN, a popular metric learning algorithm, and that combining both approaches further reduces the error.},
  author       = {Bellet, Aurélien and Habrard, Amaury and Morvant, Emilie and Sebban, Marc},
  journal      = {Machine Learning},
  number       = {1-2},
  pages        = {129 -- 154},
  publisher    = {Springer},
  title        = {{Learning a priori constrained weighted majority votes}},
  doi          = {10.1007/s10994-014-5462-z},
  volume       = {97},
  year         = {2014},
}