Learning a priori constrained weighted majority votes Bellet, Aurélien Habrard, Amaury Morvant, Emilie ; https://orcid.org/0000-0002-8301-7240 Sebban, Marc 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. Springer 2014 info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/2180 Bellet A, Habrard A, Morvant E, Sebban M. Learning a priori constrained weighted majority votes. <i>Machine Learning</i>. 2014;97(1-2):129-154. doi:<a href="https://doi.org/10.1007/s10994-014-5462-z">10.1007/s10994-014-5462-z</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/s10994-014-5462-z info:eu-repo/semantics/altIdentifier/wos/000341431300007 info:eu-repo/semantics/openAccess