[{"citation":{"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.","short":"A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014) 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.","ista":"Bellet A, Habrard A, Morvant E, Sebban M. 2014. Learning a priori constrained weighted majority votes. Machine Learning. 97(1–2), 129–154.","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.","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"},"publication":"Machine Learning","page":"129 - 154","date_published":"2014-10-01T00:00:00Z","scopus_import":1,"dc":{"type":["info:eu-repo/semantics/article","doc-type:article","text","http://purl.org/coar/resource_type/c_6501"],"identifier":["https://research-explorer.ista.ac.at/record/2180"],"description":["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."],"creator":["Bellet, Aurélien","Habrard, Amaury","Morvant, Emilie","Sebban, Marc"],"rights":["info:eu-repo/semantics/openAccess"],"source":["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"],"publisher":["Springer"],"relation":["info:eu-repo/semantics/altIdentifier/doi/10.1007/s10994-014-5462-z","info:eu-repo/grantAgreement/EC/FP7/308036"],"title":["Learning a priori constrained weighted majority votes"],"date":["2014"],"language":["eng"]},"uri_base":"https://research-explorer.ista.ac.at","day":"01","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","_id":"2180","intvolume":" 97","status":"public","oa_version":"Submitted Version","type":"journal_article","issue":"1-2","abstract":[{"lang":"eng"}],"oa":1,"main_file_link":[{"url":"https://hal.archives-ouvertes.fr/hal-01009578/document","open_access":"1"}],"project":[{"name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","language":[{}],"month":"10","acknowledgement":"This work was funded by the French project SoLSTiCe ANR-13-BS02-01 of the ANR. ","department":[{"tree":[{"_id":"ResearchGroups"},{"_id":"IST"}],"_id":"ChLa"}],"publication_status":"published","author":[{"last_name":"Bellet","first_name":"Aurélien"},{"first_name":"Amaury","last_name":"Habrard"},{"last_name":"Morvant","first_name":"Emilie","orcid":"0000-0002-8301-7240","id":"4BAC2A72-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Marc","last_name":"Sebban"}],"volume":97,"dini_type":"doc-type:article","date_created":"2018-12-11T11:56:10Z","date_updated":"2021-01-12T06:55:49Z","publist_id":"4802","ec_funded":1,"creator":{"login":"apreinsp","id":"4435EBFC-F248-11E8-B48F-1D18A9856A87"}}]