| 2009 |
|---|
| 24 | EE | András Bánhalmi,
Róbert Busa-Fekete,
Balázs Kégl:
A One-Class Classification Approach for Protein Sequences and Structures.
ISBRA 2009: 310-322 |
| 2008 |
|---|
| 23 | EE | Julien Perez,
Cécile Germain-Renaud,
Balázs Kégl,
Charles Loomis:
Grid Differentiated Services: A Reinforcement Learning Approach.
CCGRID 2008: 287-294 |
| 2007 |
|---|
| 22 | EE | Nicolas Le Roux,
Yoshua Bengio,
Pascal Lamblin,
Marc Joliveau,
Balázs Kégl:
Learning the 2-D Topology of Images.
NIPS 2007 |
| 21 | EE | Guangyi Chen,
Balázs Kégl:
Palmprint classification using contourlets.
SMC 2007: 1003-1007 |
| 20 | EE | Guangyi Chen,
Balázs Kégl:
Feature extraction using Radon, wavelet and fourier transform.
SMC 2007: 1020-1025 |
| 19 | EE | Sébastien Gambs,
Balázs Kégl,
Esma Aïmeur:
Privacy-preserving boosting.
Data Min. Knowl. Discov. 14(1): 131-170 (2007) |
| 18 | EE | Guangyi Chen,
Balázs Kégl:
Image denoising with complex ridgelets.
Pattern Recognition 40(2): 578-585 (2007) |
| 2006 |
|---|
| 17 | EE | Guangyi Chen,
Balázs Kégl:
Invariant Radon-Wavelet Packet Signatures for Pattern Recognition.
CCECE 2006: 1471-1474 |
| 16 | EE | James Bergstra,
Norman Casagrande,
Dumitru Erhan,
Douglas Eck,
Balázs Kégl:
Aggregate features and ADABOOSTfor music classification.
Machine Learning 65(2-3): 473-484 (2006) |
| 2005 |
|---|
| 15 | | Balázs Kégl,
Guy Lapalme:
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
Springer 2005 |
| 14 | EE | Norman Casagrande,
Douglas Eck,
Balázs Kégl:
Frame-Level Audio Feature Extraction Using AdaBoost.
ISMIR 2005: 345-350 |
| 2004 |
|---|
| 13 | EE | Balázs Kégl,
Ligen Wang:
Boosting on Manifolds: Adaptive Regularization of Base Classifiers.
NIPS 2004 |
| 12 | EE | Balázs Kégl:
Generalization Error and Algorithmic Convergence of Median Boosting.
NIPS 2004 |
| 2003 |
|---|
| 11 | EE | Balázs Kégl:
Robust Regression by Boosting the Median.
COLT 2003: 258-272 |
| 2002 |
|---|
| 10 | EE | 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 |
| 9 | EE | Salah Bouktif,
Houari A. Sahraoui,
Balázs Kégl:
Combining Software Quality Predictive Models: An Evolutionary Approach.
ICSM 2002: 385-392 |
| 8 | EE | Balázs Kégl:
Intrinsic Dimension Estimation Using Packing Numbers.
NIPS 2002: 681-688 |
| 7 | EE | Balázs Kégl,
Adam Krzyzak:
Piecewise Linear Skeletonization Using Principal Curves.
IEEE Trans. Pattern Anal. Mach. Intell. 24(1): 59-74 (2002) |
| 6 | EE | 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) |
| 2001 |
|---|
| 5 | EE | Balázs Kégl,
Tamás Linder,
Gábor Lugosi:
Data-Dependent Margin-Based Generalization Bounds for Classification.
COLT/EuroCOLT 2001: 368-384 |
| 2000 |
|---|
| 4 | EE | Balázs Kégl,
Adam Krzyzak,
Heinrich Niemann:
Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification.
ICPR 2000: 2081-2086 |
| 3 | EE | Balázs Kégl,
Adam Krzyzak:
Piecewise Linear Skeletonization Using Principal Curves.
ICPR 2000: 3135-3138 |
| 2 | EE | 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) |
| 1998 |
|---|
| 1 | EE | Balázs Kégl,
Adam Krzyzak,
Tamás Linder,
Kenneth Zeger:
A Polygonal Line Algorithm for Constructing Principal Curves.
NIPS 1998: 501-507 |
