---
_id: '2180'
abstract:
- lang: eng
  text: 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.
acknowledgement: 'This work was funded by the French project SoLSTiCe ANR-13-BS02-01
  of the ANR. '
article_processing_charge: No
author:
- first_name: Aurélien
  full_name: Bellet, Aurélien
  last_name: Bellet
- first_name: Amaury
  full_name: Habrard, Amaury
  last_name: Habrard
- first_name: Emilie
  full_name: Morvant, Emilie
  id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
  last_name: Morvant
  orcid: 0000-0002-8301-7240
- first_name: Marc
  full_name: Sebban, Marc
  last_name: Sebban
citation:
  ama: 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>
  apa: Bellet, A., Habrard, A., Morvant, E., &#38; Sebban, M. (2014). Learning a priori
    constrained weighted majority votes. <i>Machine Learning</i>. Springer. <a href="https://doi.org/10.1007/s10994-014-5462-z">https://doi.org/10.1007/s10994-014-5462-z</a>
  chicago: Bellet, Aurélien, Amaury Habrard, Emilie Morvant, and Marc Sebban. “Learning
    a Priori Constrained Weighted Majority Votes.” <i>Machine Learning</i>. Springer,
    2014. <a href="https://doi.org/10.1007/s10994-014-5462-z">https://doi.org/10.1007/s10994-014-5462-z</a>.
  ieee: A. Bellet, A. Habrard, E. Morvant, and M. Sebban, “Learning a priori constrained
    weighted majority votes,” <i>Machine Learning</i>, vol. 97, no. 1–2. Springer,
    pp. 129–154, 2014.
  ista: Bellet A, Habrard A, Morvant E, Sebban M. 2014. Learning a priori constrained
    weighted majority votes. Machine Learning. 97(1–2), 129–154.
  mla: Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.”
    <i>Machine Learning</i>, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:<a
    href="https://doi.org/10.1007/s10994-014-5462-z">10.1007/s10994-014-5462-z</a>.
  short: A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014)
    129–154.
corr_author: '1'
date_created: 2018-12-11T11:56:10Z
date_published: 2014-10-01T00:00:00Z
date_updated: 2025-09-29T11:35:24Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/s10994-014-5462-z
ec_funded: 1
external_id:
  isi:
  - '000341431300007'
intvolume: '        97'
isi: 1
issue: 1-2
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://hal.archives-ouvertes.fr/hal-01009578/document
month: '10'
oa: 1
oa_version: Submitted Version
page: 129 - 154
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication: Machine Learning
publication_status: published
publisher: Springer
publist_id: '4802'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning a priori constrained weighted majority votes
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 97
year: '2014'
...