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