{"language":[{"iso":"eng"}],"publication_status":"published","issue":"1-2","volume":97,"ec_funded":1,"oa_version":"Submitted Version","abstract":[{"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.","lang":"eng"}],"month":"10","intvolume":" 97","scopus_import":1,"main_file_link":[{"open_access":"1","url":"https://hal.archives-ouvertes.fr/hal-01009578/document"}],"date_updated":"2021-01-12T06:55:49Z","department":[{"_id":"ChLa"}],"_id":"2180","status":"public","type":"journal_article","day":"01","publication":"Machine Learning","year":"2014","date_published":"2014-10-01T00:00:00Z","doi":"10.1007/s10994-014-5462-z","date_created":"2018-12-11T11:56:10Z","page":"129 - 154","acknowledgement":"This work was funded by the French project SoLSTiCe ANR-13-BS02-01 of the ANR. ","quality_controlled":"1","publisher":"Springer","oa":1,"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Bellet A, Habrard A, Morvant E, Sebban M. 2014. Learning a priori constrained weighted majority votes. Machine Learning. 97(1–2), 129–154.","chicago":"Bellet, Aurélien, Amaury Habrard, Emilie Morvant, and Marc Sebban. “Learning a Priori Constrained Weighted Majority Votes.” Machine Learning. Springer, 2014. https://doi.org/10.1007/s10994-014-5462-z.","ama":"Bellet A, Habrard A, Morvant E, Sebban M. Learning a priori constrained weighted majority votes. Machine Learning. 2014;97(1-2):129-154. doi:10.1007/s10994-014-5462-z","apa":"Bellet, A., Habrard, A., Morvant, E., & Sebban, M. (2014). Learning a priori constrained weighted majority votes. Machine Learning. Springer. https://doi.org/10.1007/s10994-014-5462-z","ieee":"A. Bellet, A. Habrard, E. Morvant, and M. Sebban, “Learning a priori constrained weighted majority votes,” Machine Learning, vol. 97, no. 1–2. Springer, pp. 129–154, 2014.","short":"A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014) 129–154.","mla":"Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.” Machine Learning, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:10.1007/s10994-014-5462-z."},"title":"Learning a priori constrained weighted majority votes","author":[{"first_name":"Aurélien","full_name":"Bellet, Aurélien","last_name":"Bellet"},{"full_name":"Habrard, Amaury","last_name":"Habrard","first_name":"Amaury"},{"id":"4BAC2A72-F248-11E8-B48F-1D18A9856A87","first_name":"Emilie","last_name":"Morvant","full_name":"Morvant, Emilie","orcid":"0000-0002-8301-7240"},{"full_name":"Sebban, Marc","last_name":"Sebban","first_name":"Marc"}],"publist_id":"4802","project":[{"_id":"2532554C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding"}]}