---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Aurélien
      foaf_name: Bellet, Aurélien
      foaf_surname: Bellet
  - foaf_Person:
      foaf_givenName: Amaury
      foaf_name: Habrard, Amaury
      foaf_surname: Habrard
  - foaf_Person:
      foaf_givenName: Emilie
      foaf_name: Morvant, Emilie
      foaf_surname: Morvant
      foaf_workInfoHomepage: http://www.librecat.org/personId=4BAC2A72-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-8301-7240
  - foaf_Person:
      foaf_givenName: Marc
      foaf_name: Sebban, Marc
      foaf_surname: Sebban
  bibo_doi: 10.1007/s10994-014-5462-z
  bibo_issue: 1-2
  bibo_volume: 97
  dct_date: 2014^xs_gYear
  dct_identifier:
  - UT:000341431300007
  dct_language: eng
  dct_publisher: Springer@
  dct_title: Learning a priori constrained weighted majority votes@
...