{"_id":"2180","year":"2014","quality_controlled":"1","doi":"10.1007/s10994-014-5462-z","author":[{"last_name":"Bellet","first_name":"Aurélien","full_name":"Bellet, Aurélien"},{"first_name":"Amaury","last_name":"Habrard","full_name":"Habrard, Amaury"},{"full_name":"Morvant, Emilie","first_name":"Emilie","id":"4BAC2A72-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8301-7240","last_name":"Morvant"},{"full_name":"Sebban, Marc","last_name":"Sebban","first_name":"Marc"}],"date_updated":"2021-01-12T06:55:49Z","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","status":"public","acknowledgement":"This work was funded by the French project SoLSTiCe ANR-13-BS02-01 of the ANR. ","publication_status":"published","main_file_link":[{"url":"https://hal.archives-ouvertes.fr/hal-01009578/document","open_access":"1"}],"scopus_import":1,"volume":97,"citation":{"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>","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>","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>.","short":"A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014) 129–154.","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>."},"month":"10","publication":"Machine Learning","issue":"1-2","page":"129 - 154","day":"01","project":[{"call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding"}],"intvolume":"        97","language":[{"iso":"eng"}],"date_created":"2018-12-11T11:56:10Z","title":"Learning a priori constrained weighted majority votes","department":[{"_id":"ChLa"}],"ec_funded":1,"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,"date_published":"2014-10-01T00:00:00Z","oa_version":"Submitted Version","publist_id":"4802","type":"journal_article","publisher":"Springer"}