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A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost.

R. Busa-Fekete, B. Kégl, T. Éltetö, and G. Szarvas. ECML/PKDD (1), volume 6911 of Lecture Notes in Computer Science, page 263-279. Springer, (2011)

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Privacy-preserving boosting.S. Gambs, B. Kégl, and E. Aïmeur. Data Min. Knowl. Discov., 14 (1): 131-170 (2007)Piecewise Linear Skeletonization Using Principal Curves.B. Kégl, and A. Krzyzak. ICPR, page 3135-3138. IEEE Computer Society, (2000)Palmprint classification using contourlets.G. Chen, and B. Kégl. SMC, page 1003-1007. IEEE, (2007)Geometry in sound: a speech/Music audio Classifier Inspired by an Image Classifier.N. Casagrande, D. Eck, and B. Kégl. ICMC, Michigan Publishing, (2005)Similarity encoding for learning with dirty categorical variables.P. Cerda, G. Varoquaux, and B. Kégl. CoRR, (2018)Gossip-based distributed stochastic bandit algorithms.B. Szörényi, R. Busa-Fekete, I. Hegedüs, R. Ormándi, M. Jelasity, and B. Kégl. ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 19-27. JMLR.org, (2013)Adaptive Metropolis with Online Relabeling.R. Bardenet, O. Cappé, G. Fort, and B. Kégl. AISTATS, volume 22 of JMLR Proceedings, page 91-99. JMLR.org, (2012)The return of AdaBoost.MH: multi-class Hamming trees.B. Kégl. ICLR (Poster), (2014)Radial basis function networks in nonparametric classification and function learning.B. Kégl, A. Krzyzak, and H. Niemann. ICPR, page 565-570. IEEE Computer Society, (1998)Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification.B. Kégl, A. Krzyzak, and H. Niemann. ICPR, page 2081-2086. IEEE Computer Society, (2000)

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Disambiguation of "Kégl, Balázs"

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A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost.

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A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost.