Balázs Kégl

Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl: MULTIBOOST: A Multi-purpose Boosting Package. Journal of Machine Learning Research 13: 549-553 (2012)

Rémi Bardenet, Olivier Cappé, Gersende Fort, Balázs Kégl: Adaptive Metropolis with Online Relabeling. Journal of Machine Learning Research - Proceedings Track 22: 91-99 (2012)

István Hegedüs, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity, Balázs Kégl: Peer-to-Peer Multi-class Boosting. Euro-Par 2012: 389-400

Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl: Fast classification using sparse decision DAGs. ICML 2012

Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas: Ranking by calibrated AdaBoost. Journal of Machine Learning Research - Proceedings Track 14: 37-48 (2011)

James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl: Algorithms for Hyper-Parameter Optimization. NIPS 2011: 2546-2554

Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas: A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost. ECML/PKDD (1) 2011: 263-279

Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis: Multi-objective Reinforcement Learning for Responsive Grids. J. Grid Comput. 8(3): 473-492 (2010)

Guangyi Chen, Balázs Kégl: Invariant pattern recognition using contourlets and AdaBoost. Pattern Recognition 43(3): 579-583 (2010)

Rémi Bardenet, Balázs Kégl: Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. ICML 2010: 55-62

Róbert Busa-Fekete, Balázs Kégl: Fast boosting using adversarial bandits. ICML 2010: 143-150

Guangyi Chen, Wei-Ping Zhu, Balázs Kégl, Róbert Busa-Fekete: Palmprint Classification Using Wavelets and AdaBoost. ISNN (2) 2010: 178-183

Róbert Busa-Fekete, Balázs Kégl: Accelerating AdaBoost using UCB. Journal of Machine Learning Research - Proceedings Track 7: 111-122 (2009)

Balázs Kégl, Róbert Busa-Fekete: Boosting products of base classifiers. ICML 2009: 63

András Bánhalmi, Róbert Busa-Fekete, Balázs Kégl: A One-Class Classification Approach for Protein Sequences and Structures. ISBRA 2009: 310-322

Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis: Grid Differentiated Services: A Reinforcement Learning Approach. CCGRID 2008: 287-294

Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis: Utility-Based Reinforcement Learning for Reactive Grids. ICAC 2008: 205-206

Sébastien Gambs, Balázs Kégl, Esma Aïmeur: Privacy-preserving boosting. Data Min. Knowl. Discov. 14(1): 131-170 (2007)

Guangyi Chen, Balázs Kégl: Image denoising with complex ridgelets. Pattern Recognition 40(2): 578-585 (2007)

Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl: Learning the 2-D Topology of Images. NIPS 2007

Guangyi Chen, Balázs Kégl: Palmprint classification using contourlets. SMC 2007: 1003-1007

Guangyi Chen, Balázs Kégl: Feature extraction using Radon, wavelet and fourier transform. SMC 2007: 1020-1025

James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl: Aggregate features and ADABOOSTfor music classification. Machine Learning 65(2-3): 473-484 (2006)

Guangyi Chen, Balázs Kégl: Invariant Radon-Wavelet Packet Signatures for Pattern Recognition. CCECE 2006: 1471-1474

Norman Casagrande, Douglas Eck, Balázs Kégl: Frame-Level Audio Feature Extraction Using AdaBoost. ISMIR 2005: 345-350

Balázs Kégl, Guy Lapalme (Eds.): Advances in Artificial Intelligence, 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, Canada, May 9-11, 2005, Proceedings. Lecture Notes in Computer Science 3501, Springer 2005, isbn 3-540-25864-7

Balázs Kégl: Generalization Error and Algorithmic Convergence of Median Boosting. NIPS 2004

Balázs Kégl, Ligen Wang: Boosting on Manifolds: Adaptive Regularization of Base Classifiers. NIPS 2004

Balázs Kégl: Robust Regression by Boosting the Median. COLT 2003: 258-272

András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi: Data-dependent margin-based generalization bounds for classification. Journal of Machine Learning Research 3: 73-98 (2002)

Balázs Kégl, Adam Krzyzak: Piecewise Linear Skeletonization Using Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. 24(1): 59-74 (2002)

Salah Bouktif, Houari A. Sahraoui, Balázs Kégl: Combining Software Quality Predictive Models: An Evolutionary Approach. ICSM 2002: 385-392

Danielle Azar, Doina Precup, Salah Bouktif, Balázs Kégl, Houari A. Sahraoui: Combining and Adapting Software Quality Predictive Models by Genetic Algorithms. ASE 2002: 285-288

Balázs Kégl: Intrinsic Dimension Estimation Using Packing Numbers. NIPS 2002: 681-688

Adam Krzyzak, Jerzy Z. Sasiadek, Balázs Kégl: Non-parametric identification of dynamic non-linear systems by a Hermite Series Approach. Int. J. Systems Science 32(10): 1261-1285 (2001)

Balázs Kégl, Tamás Linder, Gábor Lugosi: Data-Dependent Margin-Based Generalization Bounds for Classification. COLT/EuroCOLT 2001: 368-384

Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger: Learning and Design of Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. 22(3): 281-297 (2000)

Balázs Kégl, Adam Krzyzak, Heinrich Niemann: Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification. ICPR 2000: 2081-2086

Balázs Kégl, Adam Krzyzak: Piecewise Linear Skeletonization Using Principal Curves. ICPR 2000: 3135-3138

Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger: A Polygonal Line Algorithm for Constructing Principal Curves. NIPS 1998: 501-507