{"conference":{"end_date":"2014-08-22","name":"IAPR: International Workshop on Structural, Syntactic, and Statistical Pattern Recognition","start_date":"2014-08-20","location":"Joensuu, Finland"},"author":[{"orcid":"0000-0002-8301-7240","first_name":"Emilie","last_name":"Morvant","full_name":"Morvant, Emilie","id":"4BAC2A72-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Habrard, Amaury","first_name":"Amaury","last_name":"Habrard"},{"full_name":"Ayache, Stéphane","first_name":"Stéphane","last_name":"Ayache"}],"status":"public","external_id":{"arxiv":["1404.7796"]},"publist_id":"4989","date_updated":"2021-01-12T06:55:01Z","alternative_title":["LNCS"],"oa_version":"Preprint","type":"conference","doi":"10.1007/978-3-662-44415-3_16","page":"153 - 162","project":[{"grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425","name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7"}],"main_file_link":[{"url":"http://arxiv.org/abs/1404.7796","open_access":"1"}],"title":"Majority vote of diverse classifiers for late fusion","quality_controlled":"1","date_published":"2014-01-01T00:00:00Z","citation":{"apa":"Morvant, E., Habrard, A., &#38; Ayache, S. (2014). Majority vote of diverse classifiers for late fusion. In <i>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i> (Vol. 8621, pp. 153–162). Joensuu, Finland: Springer. <a href=\"https://doi.org/10.1007/978-3-662-44415-3_16\">https://doi.org/10.1007/978-3-662-44415-3_16</a>","ista":"Morvant E, Habrard A, Ayache S. 2014. Majority vote of diverse classifiers for late fusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). IAPR: International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, LNCS, vol. 8621, 153–162.","chicago":"Morvant, Emilie, Amaury Habrard, and Stéphane Ayache. “Majority Vote of Diverse Classifiers for Late Fusion.” In <i>Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>, 8621:153–62. Springer, 2014. <a href=\"https://doi.org/10.1007/978-3-662-44415-3_16\">https://doi.org/10.1007/978-3-662-44415-3_16</a>.","ieee":"E. Morvant, A. Habrard, and S. Ayache, “Majority vote of diverse classifiers for late fusion,” in <i>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>, Joensuu, Finland, 2014, vol. 8621, pp. 153–162.","ama":"Morvant E, Habrard A, Ayache S. Majority vote of diverse classifiers for late fusion. In: <i>Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>. Vol 8621. Springer; 2014:153-162. doi:<a href=\"https://doi.org/10.1007/978-3-662-44415-3_16\">10.1007/978-3-662-44415-3_16</a>","mla":"Morvant, Emilie, et al. “Majority Vote of Diverse Classifiers for Late Fusion.” <i>Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>, vol. 8621, Springer, 2014, pp. 153–62, doi:<a href=\"https://doi.org/10.1007/978-3-662-44415-3_16\">10.1007/978-3-662-44415-3_16</a>.","short":"E. Morvant, A. Habrard, S. Ayache, in:, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 153–162."},"intvolume":"      8621","year":"2014","ec_funded":1,"month":"01","date_created":"2018-12-11T11:55:28Z","abstract":[{"text":"In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle this problem, we investigate a recent and elegant well-founded quadratic program named MinCq coming from the machine learning PAC-Bayesian theory. MinCq looks for the weighted combination, over a set of real-valued functions seen as voters, leading to the lowest misclassification rate, while maximizing the voters’ diversity. We propose an extension of MinCq tailored to multimedia indexing. Our method is based on an order-preserving pairwise loss adapted to ranking that allows us to improve Mean Averaged Precision measure while taking into account the diversity of the voters that we want to fuse. We provide evidence that this method is naturally adapted to late fusion procedures and confirm the good behavior of our approach on the challenging PASCAL VOC’07 benchmark.","lang":"eng"}],"volume":8621,"_id":"2057","scopus_import":1,"oa":1,"publication":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","language":[{"iso":"eng"}],"publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"ChLa"}],"publisher":"Springer","day":"01"}