{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T14:24:16Z","timestamp":1777645456448,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"1-2","license":[{"start":{"date-parts":[[2009,10,1]],"date-time":"2009-10-01T00:00:00Z","timestamp":1254355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Fundamenta Informaticae"],"published-print":{"date-parts":[[2009,10]]},"abstract":"<jats:p>We focus on the adaptation of boosting to representation spaces composed of different subsets of features. Rather than imposing a single weak learner to handle data that could come from different sources (e.g., images and texts and sounds), we suggest the decomposition of the learning task into several dependent sub-problems of boosting, treated by different weak learners, that will optimally collaborate during the weight update stage. To achieve this task, we introduce a new weighting scheme for which we provide theoretical results. Experiments are carried out and show that ourmethod works significantly better than any combination of independent boosting procedures.<\/jats:p>","DOI":"10.3233\/fi-2009-169","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T22:59:02Z","timestamp":1575327542000},"page":"89-109","source":"Crossref","is-referenced-by-count":2,"title":["Boosting Classifiers Built from Different Subsets of \t\t\t Features"],"prefix":"10.1177","volume":"96","author":[{"given":"ean-Christophe","family":"Janodet","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Lyon, F-69003, Lyon, France\r\t\t\t Universit\u00e9 de Saint-Etienne, F-42000, St-Etienne, France. UMR-CNRS\r\t\t\t 5516, Laboratoire Hubert Curien 18 rue du Professeur Benoit Lauras, F-42000,\r\t\t\t St-Etienne, France. E-mail: {janodet,Marc.Sebban}@univ-st-etienne.fr"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Sebban","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lyon, F-69003, Lyon, France\r\t\t\t Universit\u00e9 de Saint-Etienne, F-42000, St-Etienne, France. UMR-CNRS\r\t\t\t 5516, Laboratoire Hubert Curien 18 rue du Professeur Benoit Lauras, F-42000,\r\t\t\t St-Etienne, France. E-mail: {janodet,Marc.Sebban}@univ-st-etienne.fr"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henri-Maxime","family":"Suchier","sequence":"additional","affiliation":[{"name":"Artefacto, 11 rue Meynier, F-35700 Rennes, France.\r\t\t\tE-mail: hm.suchier@artefacto.fr"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2009,10,1]]},"container-title":["Fundamenta Informaticae"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/FI-2009-169","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/FI-2009-169","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:32:16Z","timestamp":1777444336000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/FI-2009-169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,10]]},"references-count":0,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2009,10]]}},"alternative-id":["10.3233\/FI-2009-169"],"URL":"https:\/\/doi.org\/10.3233\/fi-2009-169","relation":{},"ISSN":["0169-2968","1875-8681"],"issn-type":[{"value":"0169-2968","type":"print"},{"value":"1875-8681","type":"electronic"}],"subject":[],"published":{"date-parts":[[2009,10]]}}}