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In 14th International Conference on Machine Learning (ICML), pages 313\u2013321, Tennessee, USA, 1997."},{"key":"2_CR97","doi-asserted-by":"crossref","unstructured":"R.\u00a0Schapire. Theoretical views of boosting. In Proceedings of the 4th European Conference on Computational Learning Theory, EuroCOLT \u201999, pages 1\u201310, London, UK, 1999. Springer-Verlag.","DOI":"10.1007\/3-540-49097-3_1"},{"key":"2_CR98","doi-asserted-by":"crossref","unstructured":"R.\u00a0Schapire. The boosting approach to machine learning: An overview. In Nonlinear Estimation and Classification, Berkeley, 2002. Springer.","DOI":"10.1007\/978-0-387-21579-2_9"},{"key":"2_CR99","unstructured":"R.\u00a0Schapire, Y.\u00a0Freund, P.\u00a0Bartlett, and W.\u00a0Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proceedings of the 14th International Conference on Machine Learning (ICML), pages 322\u2013330, Nashville, TN, 1997."},{"issue":"3","key":"2_CR100","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1023\/A:1007614523901","volume":"37","author":"R Schapire","year":"1999","unstructured":"R.\u00a0Schapire and Y.\u00a0Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297\u2013336, 1999.","journal-title":"Machine Learning"},{"issue":"2\/3","key":"2_CR101","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1023\/A:1007649029923","volume":"39","author":"R Schapire","year":"2000","unstructured":"R.\u00a0Schapire and Y.\u00a0Singer. BoosTexter: A Boosting-based System for Text Categorization. Machine Learning, 39(2\/3):135\u2013168, 2000.","journal-title":"Machine Learning"},{"key":"2_CR102","doi-asserted-by":"crossref","unstructured":"B. Sch\u00f6lkopf and A. Smola. Learning with Kernels. 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In Y.\u00a0Bengio, D.\u00a0Schuurmans, J.\u00a0Lafferty, C.\u00a0Williams, and A.\u00a0Culotta, editors, Advances in Neural Information Processing Systems (NIPS\u201909), pages 1651\u20131659, Vancouver, BC, Canada, December 2009. MIT Press."},{"key":"2_CR106","doi-asserted-by":"crossref","unstructured":"J.\u00a0Sochman and J.\u00a0Matas. \u201cWaldboost\u201d learning for time constrained sequential detection. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905) \u2013 Volume 2,\u00a0pages 150\u2013156, Washington, DC, USA, 2005. IEEE Computer Society.","DOI":"10.1109\/CVPR.2005.373"},{"key":"2_CR107","doi-asserted-by":"crossref","unstructured":"A.\u00a0Stefan, V.\u00a0Athitsos, Q.\u00a0Yuan, and S.\u00a0Sclaroff. Reducing jointboost-based multiclass classification to proximity search. In Computer Vision and Pattern Recognition (CVPR), pages 589\u2013596. 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Unifying the error-correcting and output-code adaboost within the margin framework. In Proceedings of the 22nd International Conference on Machine Learning (ICML), pages 872\u2013879, New York, NY, USA, 2005. ACM.","DOI":"10.1145\/1102351.1102461"},{"key":"2_CR111","unstructured":"J.\u00a0Thongkam, O.\u00a0Xu, Y.\u00a0Zhang, F.\u00a0Huang, and G.\u00a0Adaboosts. Breast cancer survivability via adaboost algorithms. In Proceedings of the second Australasian workshop on Health data and knowledge management \u2013 Volume 80, HDKM \u201908, pages 55\u201364, Darlinghurst, Australia, 2008. Australian Computer Society, Inc."},{"key":"2_CR112","doi-asserted-by":"crossref","unstructured":"K.\u00a0Tieu and P.\u00a0Viola. Boosting image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition \u2013 CVPR, volume\u00a01, pages 228\u2013235, 2000.","DOI":"10.1109\/CVPR.2000.855824"},{"issue":"5","key":"2_CR113","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1109\/TPAMI.2007.1055","volume":"29","author":"A Torralba","year":"2007","unstructured":"A.\u00a0Torralba, K.\u00a0Murphy, and W.\u00a0Freeman. Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5):854 \u2013 869, March 2007.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"11","key":"2_CR114","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1145\/1968.1972","volume":"27","author":"L Valiant","year":"1984","unstructured":"L.\u00a0Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134\u20131142, 1984.","journal-title":"Communications of the ACM"},{"key":"2_CR115","doi-asserted-by":"crossref","unstructured":"H.\u00a0Valizadegan, R.\u00a0Jin, and A.\u00a0K. Jain. Semi-Supervised Boosting for Multi-Class Classification. In ECML PKDD \u201908: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases \u2013 Part II, pages 522\u2013537, Berlin, Heidelberg, 2008. Springer-Verlag.","DOI":"10.1007\/978-3-540-87481-2_34"},{"key":"2_CR116","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"1999","unstructured":"V.\u00a0Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, NY, 1999."},{"issue":"2","key":"2_CR117","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1137\/1116025","volume":"16","author":"V Vapnik","year":"1971","unstructured":"V.\u00a0Vapnik and A.\u00a0Chervonenkis. 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In Y.\u00a0Weiss, B.\u00a0Sch\u00f6lkopf, and J.\u00a0Platt, editors, Advances in Neural Information Processing Systems 18, pages 1417\u20131424, Cambridge, MA, 2006. MIT Press."},{"key":"2_CR124","unstructured":"L.\u00a0Wang, S.\u00a0Yuan, L.\u00a0Li, and H.\u00a0Li. Boosting na\u00efve Bayes by active learning. In Third International Conference on Machine Learning and Cybernetics, volume\u00a01, pages 41\u201348, Shanghai, China, 2004."},{"key":"2_CR125","doi-asserted-by":"crossref","unstructured":"P.\u00a0Wang, C.\u00a0Shen, N.\u00a0Barnes, H.\u00a0Zheng, and Z.\u00a0Ren. Asymmetric totally-corrective boosting for real-time object detection. In Asian Conference on Computer Vision (ACCV), pages I: 176\u2013188, 2010.","DOI":"10.1007\/978-3-642-19315-6_14"},{"key":"2_CR126","unstructured":"M.\u00a0Warmuth, K.\u00a0Glocer, and G.\u00a0R\u00e4tsch. Boosting algorithms for maximizing the soft margin. In Advances in Neural Information Processing Systems NIPS, pages 1\u20138, MIT Press, 2007."},{"key":"2_CR127","doi-asserted-by":"crossref","unstructured":"M.\u00a0Warmuth, K.\u00a0Glocer, and S.\u00a0Vishwanathan. Entropy regularized LPBoost. In Proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT \u201908, pages 256\u2013271, Springer-Verlag, Berlin, Heidelberg, 2008.","DOI":"10.1007\/978-3-540-87987-9_23"},{"key":"2_CR128","doi-asserted-by":"crossref","unstructured":"M.\u00a0Warmuth, J.\u00a0Liao, and G.\u00a0R\u00e4tsch. Totally corrective boosting algorithms that maximize the margin. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 1001\u20131008, New York, NY, USA, 2006. ACM.","DOI":"10.1145\/1143844.1143970"},{"key":"2_CR129","doi-asserted-by":"crossref","unstructured":"J.\u00a0Warrell, P.\u00a0Torr, and S.\u00a0Prince. Styp-boost: A bilinear boosting algorithm for learning style-parameterized classifiers. In British Machine Vision Conference (BMVC), 2010.","DOI":"10.5244\/C.24.60"},{"issue":"1","key":"2_CR130","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-005-4258-6","volume":"58","author":"J Webb","year":"2005","unstructured":"J.\u00a0Webb, J.\u00a0Boughton, and Z.\u00a0Wang. Not so na\u00efve Bayes: Aggregating one-dependence estimators. Machine Learning, 58(1):5\u201324, 2005.","journal-title":"Machine Learning"},{"key":"2_CR131","doi-asserted-by":"crossref","unstructured":"P.\u00a0Yang, S.\u00a0Shan, W.\u00a0Gao, S.\u00a0Z. Li, and D.\u00a0Zhang. Face recognition using ada-boosted gabor features. In Proceedings of the 16th International Conference on Face and Gesture Recognition, pages 356\u2013361, 2004.","DOI":"10.1109\/AFGR.2004.1301556"},{"issue":"8","key":"2_CR132","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1109\/TMM.2008.2007344","volume":"10","author":"C Zhang","year":"2008","unstructured":"C.\u00a0Zhang, P.\u00a0Yin, Y.\u00a0Rui, R.\u00a0Cutler, P.\u00a0Viola, X.\u00a0Sun, N.\u00a0Pinto, and Z.\u00a0Zhang. Boosting-based multimodal speaker detection for distributed meeting videos. IEEE Transactions on Multimedia, 10(8):1541\u20131552, December 2008.","journal-title":"IEEE Transactions on Multimedia"},{"key":"2_CR133","doi-asserted-by":"crossref","unstructured":"C.\u00a0Zhang and Z.\u00a0Zhang. Boosting-Based Face Detection and Adaptation. 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In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201909, pages 1017\u20131026, New York, NY, USA, 2009. ACM.","DOI":"10.1145\/1557019.1557129"},{"key":"2_CR137","unstructured":"M.\u00a0Zhou, H.\u00a0Wei, and S.\u00a0Maybank. Gabor wavelets and AdaBoost in feature selection for face verification. In Proceedings of the Workshop on Applications of Computer Visions, pages 101\u2013109, Graz, Austria, 2006."},{"key":"2_CR138","doi-asserted-by":"publisher","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","volume":"2","author":"J Zhu","year":"2009","unstructured":"J.\u00a0Zhu, H.\u00a0Zou, S.\u00a0Rosset, and T.\u00a0Hastie. Multi-class adaboost. Statistics and Its Interface, 2:349\u2013360, 2009.","journal-title":"Statistics and Its Interface"},{"key":"2_CR139","doi-asserted-by":"crossref","unstructured":"X.\u00a0Zhu, C.\u00a0Bao, and W.\u00a0Qiu. Bagging very weak learners with lazy local learning. 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