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In contrast to the traditional models, CDE can make dynamic adjustments to accommodate new semantics, to assist the discovery of useful low-level features, and to improve class-prediction accuracy. We depict two key components of CDE: a multi-level function that asserts class-prediction confidence, and the dynamic ensemble method based upon the confidence function. Through theoretical analysis and empirical study, we demonstrate that CDE is effective in annotating large-scale, real-world image datasets.<\/jats:p>","DOI":"10.1145\/1062253.1062257","type":"journal-article","created":{"date-parts":[[2005,8,1]],"date-time":"2005-08-01T15:53:11Z","timestamp":1122911591000},"page":"168-189","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Semantics and feature discovery via confidence-based ensemble"],"prefix":"10.1145","volume":"1","author":[{"given":"Kingshy","family":"Goh","sequence":"first","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beitao","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward Y.","family":"Chang","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2005,5]]},"reference":[{"volume-title":"Proceedings of the IEEE International Conference on Multimedia. 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