| 2009 |
|---|
| 146 | EE | Bharath K. Sriperumbudur,
Arthur Gretton,
Kenji Fukumizu,
Gert R. G. Lanckriet,
Bernhard Schölkopf:
A note on integral probability metrics and $\phi$-divergences
CoRR abs/0901.2698: (2009) |
| 2008 |
|---|
| 145 | EE | Guillaume Charpiat,
Matthias Hofmann,
Bernhard Schölkopf:
Automatic Image Colorization Via Multimodal Predictions.
ECCV (3) 2008: 126-139 |
| 144 | EE | Duy Nguyen-Tuong,
Jan Peters,
Matthias Seeger,
Bernhard Schölkopf:
Learning Inverse Dynamics: a Comparison.
ESANN 2008: 13-18 |
| 143 | EE | Pia Breuer,
Kwang In Kim,
Wolf Kienzle,
Bernhard Schölkopf,
Volker Blanz:
Automatic 3D face reconstruction from single images or video.
FG 2008: 1-8 |
| 142 | EE | Christian Walder,
Kwang In Kim,
Bernhard Schölkopf:
Sparse multiscale gaussian process regression.
ICML 2008: 1112-1119 |
| 141 | EE | Le Song,
Xinhua Zhang,
Alex J. Smola,
Arthur Gretton,
Bernhard Schölkopf:
Tailoring density estimation via reproducing kernel moment matching.
ICML 2008: 992-999 |
| 140 | EE | Gabriele Schweikert,
Christian Widmer,
Bernhard Schölkopf,
Gunnar Rätsch:
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis.
NIPS 2008: 1433-1440 |
| 139 | EE | Matthias W. Seeger,
Hannes Nickisch,
Rolf Pohmann,
Bernhard Schölkopf:
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences.
NIPS 2008: 1441-1448 |
| 138 | EE | Christian Walder,
Bernhard Schölkopf:
Diffeomorphic Dimensionality Reduction.
NIPS 2008: 1713-1720 |
| 137 | EE | Kenji Fukumizu,
Bharath K. Sriperumbudur,
Arthur Gretton,
Bernhard Schölkopf:
Characteristic Kernels on Groups and Semigroups.
NIPS 2008: 473-480 |
| 136 | EE | N. Jeremy Hill,
Jason Farquhar,
Suzanna Martens,
Felix Bießmann,
Bernhard Schölkopf:
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance.
NIPS 2008: 665-672 |
| 135 | EE | Patrik O. Hoyer,
Dominik Janzing,
Joris M. Mooij,
Jonas Peters,
Bernhard Schölkopf:
Nonlinear causal discovery with additive noise models.
NIPS 2008: 689-696 |
| 134 | EE | Dominik Janzing,
Bernhard Schölkopf:
Causal inference using the algorithmic Markov condition
CoRR abs/0804.3678: (2008) |
| 133 | EE | Arthur Gretton,
Karsten M. Borgwardt,
Malte J. Rasch,
Bernhard Schölkopf,
Alexander J. Smola:
A Kernel Method for the Two-Sample Problem
CoRR abs/0805.2368: (2008) |
| 132 | EE | Florian Steinke,
Matthias Hein,
Jan Peters,
Bernhard Schölkopf:
Manifold-valued Thin-Plate Splines with Applications in Computer Graphics.
Comput. Graph. Forum 27(2): 437-448 (2008) |
| 131 | EE | William T. Freeman,
Pietro Perona,
Bernhard Schölkopf:
Guest Editorial.
International Journal of Computer Vision 77(1-3): 1 (2008) |
| 130 | EE | Xiaohai Sun,
Dominik Janzing,
Bernhard Schölkopf:
Causal reasoning by evaluating the complexity of conditional densities with kernel methods.
Neurocomputing 71(7-9): 1248-1256 (2008) |
| 129 | EE | Florian Steinke,
Bernhard Schölkopf:
Kernels, regularization and differential equations.
Pattern Recognition 41(11): 3271-3286 (2008) |
| 2007 |
|---|
| 128 | | Bernhard Schölkopf,
John C. Platt,
Thomas Hoffman:
Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006
MIT Press 2007 |
| 127 | | Arthur Gretton,
Karsten M. Borgwardt,
Malte J. Rasch,
Bernhard Schölkopf,
Alexander J. Smola:
A Kernel Approach to Comparing Distributions.
AAAI 2007: 1637-1641 |
| 126 | EE | Alex J. Smola,
Arthur Gretton,
Le Song,
Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions.
ALT 2007: 13-31 |
| 125 | EE | Jan Peters,
Stefan Schaal,
Bernhard Schölkopf:
Towards Machine Learning of Motor Skills.
AMS 2007: 138-144 |
| 124 | EE | Wolf Kienzle,
Bernhard Schölkopf,
Felix A. Wichmann,
Matthias O. Franz:
How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye Movements.
DAGM-Symposium 2007: 405-414 |
| 123 | EE | Alexander J. Smola,
Arthur Gretton,
Le Song,
Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions.
Discovery Science 2007: 40-41 |
| 122 | EE | Xiaohai Sun,
Dominik Janzing,
Bernhard Schölkopf:
Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions.
ESANN 2007: 441-446 |
| 121 | EE | Mingrui Wu,
Kai Yu,
Shipeng Yu,
Bernhard Schölkopf:
Local learning projections.
ICML 2007: 1039-1046 |
| 120 | EE | Xiaohai Sun,
Dominik Janzing,
Bernhard Schölkopf,
Kenji Fukumizu:
A kernel-based causal learning algorithm.
ICML 2007: 855-862 |
| 119 | EE | Arthur Gretton,
Kenji Fukumizu,
Choon Hui Teo,
Le Song,
Bernhard Schölkopf,
Alex J. Smola:
A Kernel Statistical Test of Independence.
NIPS 2007 |
| 118 | EE | Fabian H. Sinz,
Olivier Chapelle,
Alekh Agarwal,
Bernhard Schölkopf:
An Analysis of Inference with the Universum.
NIPS 2007 |
| 117 | EE | Kenji Fukumizu,
Arthur Gretton,
Xiaohai Sun,
Bernhard Schölkopf:
Kernel Measures of Conditional Dependence.
NIPS 2007 |
| 2006 |
|---|
| 116 | EE | N. Jeremy Hill,
Thomas Navin Lal,
Michael Schröder,
Thilo Hinterberger,
Guido Widman,
Christian Erich Elger,
Bernhard Schölkopf,
Niels Birbaumer:
Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals.
DAGM-Symposium 2006: 404-413 |
| 115 | EE | Karsten M. Borgwardt,
Arthur Gretton,
Malte J. Rasch,
Hans-Peter Kriegel,
Bernhard Schölkopf,
Alexander J. Smola:
Integrating structured biological data by Kernel Maximum Mean Discrepancy.
ISMB (Supplement of Bioinformatics) 2006: 49-57 |
| 114 | EE | Florian Steinke,
Bernhard Schölkopf,
Volker Blanz:
Learning Dense 3D Correspondence.
NIPS 2006: 1313-1320 |
| 113 | EE | Mingrui Wu,
Bernhard Schölkopf:
A Local Learning Approach for Clustering.
NIPS 2006: 1529-1536 |
| 112 | EE | Dengyong Zhou,
Jiayuan Huang,
Bernhard Schölkopf:
Learning with Hypergraphs: Clustering, Classification, and Embedding.
NIPS 2006: 1601-1608 |
| 111 | EE | Christian Walder,
Bernhard Schölkopf,
Olivier Chapelle:
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions.
NIPS 2006: 273-280 |
| 110 | EE | Arthur Gretton,
Karsten M. Borgwardt,
Malte J. Rasch,
Bernhard Schölkopf,
Alexander J. Smola:
A Kernel Method for the Two-Sample-Problem.
NIPS 2006: 513-520 |
| 109 | EE | Jiayuan Huang,
Alexander J. Smola,
Arthur Gretton,
Karsten M. Borgwardt,
Bernhard Schölkopf:
Correcting Sample Selection Bias by Unlabeled Data.
NIPS 2006: 601-608 |
| 108 | EE | Wolf Kienzle,
Felix A. Wichmann,
Bernhard Schölkopf,
Matthias O. Franz:
A Nonparametric Approach to Bottom-Up Visual Saliency.
NIPS 2006: 689-696 |
| 107 | EE | Christian Walder,
Bernhard Schölkopf,
Olivier Chapelle:
Implicit Surface Modelling with a Globally Regularised Basis of Compact Support.
Comput. Graph. Forum 25(3): 635-644 (2006) |
| 106 | EE | Sören Sonnenburg,
Gunnar Rätsch,
Christin Schäfer,
Bernhard Schölkopf:
Large Scale Multiple Kernel Learning.
Journal of Machine Learning Research 7: 1531-1565 (2006) |
| 105 | EE | Mingrui Wu,
Bernhard Schölkopf,
Gökhan H. Bakir:
A Direct Method for Building Sparse Kernel Learning Algorithms.
Journal of Machine Learning Research 7: 603-624 (2006) |
| 104 | EE | Arnulf B. A. Graf,
Felix A. Wichmann,
Heinrich H. Bülthoff,
Bernhard Schölkopf:
Classification of Faces in Man and Machine.
Neural Computation 18(1): 143-165 (2006) |
| 103 | EE | Matthias O. Franz,
Bernhard Schölkopf:
A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression.
Neural Computation 18(12): 3097-3118 (2006) |
| 2005 |
|---|
| 102 | EE | Arthur Gretton,
Olivier Bousquet,
Alex J. Smola,
Bernhard Schölkopf:
Measuring Statistical Dependence with Hilbert-Schmidt Norms.
ALT 2005: 63-77 |
| 101 | EE | Dengyong Zhou,
Bernhard Schölkopf:
Regularization on Discrete Spaces.
DAGM-Symposium 2005: 361-368 |
| 100 | EE | Koji Tsuda,
Hyunjung Shin,
Bernhard Schölkopf:
Fast protein classification with multiple networks.
ECCB/JBI 2005: 65 |
| 99 | EE | Wolf Kienzle,
Bernhard Schölkopf:
Training Support Vector Machines with Multiple Equality Constraints.
ECML 2005: 182-193 |
| 98 | EE | Dengyong Zhou,
Jiayuan Huang,
Bernhard Schölkopf:
Learning from labeled and unlabeled data on a directed graph.
ICML 2005: 1036-1043 |
| 97 | EE | Thomas Navin Lal,
Michael Schröder,
N. Jeremy Hill,
Hubert Preißl,
Thilo Hinterberger,
Jürgen Mellinger,
Martin Bogdan,
Wolfgang Rosenstiel,
Thomas Hofmann,
Niels Birbaumer,
Bernhard Schölkopf:
A brain computer interface with online feedback based on magnetoencephalography.
ICML 2005: 465-472 |
| 96 | EE | Bernhard Schölkopf,
Florian Steinke,
Volker Blanz:
Object correspondence as a machine learning problem.
ICML 2005: 776-783 |
| 95 | EE | Sören Sonnenburg,
Gunnar Rätsch,
Bernhard Schölkopf:
Large scale genomic sequence SVM classifiers.
ICML 2005: 848-855 |
| 94 | EE | Christian Walder,
Olivier Chapelle,
Bernhard Schölkopf:
Implicit surface modelling as an eigenvalue problem.
ICML 2005: 936-939 |
| 93 | EE | Mingrui Wu,
Bernhard Schölkopf,
Gökhan H. Bakir:
Building Sparse Large Margin Classifiers.
ICML 2005: 996-1003 |
| 92 | EE | Gunnar Rätsch,
Sören Sonnenburg,
Bernhard Schölkopf:
RASE: recognition of alternatively spliced exons in C.elegans.
ISMB (Supplement of Bioinformatics) 2005: 369-377 |
| 91 | EE | Jason Weston,
Bernhard Schölkopf,
Olivier Bousquet:
Joint Kernel Maps.
IWANN 2005: 176-191 |
| 90 | EE | Tobias Jung,
Luis Herrera,
Bernhard Schölkopf:
Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach.
IWANN 2005: 960-967 |
| 89 | EE | Joaquin Quiñonero Candela,
Carl Edward Rasmussen,
Fabian H. Sinz,
Olivier Bousquet,
Bernhard Schölkopf:
Evaluating Predictive Uncertainty Challenge.
MLCW 2005: 1-27 |
| 88 | EE | Florian Steinke,
Bernhard Schölkopf,
Volker Blanz:
Support Vector Machines for 3D Shape Processing.
Comput. Graph. Forum 24(3): 285-294 (2005) |
| 87 | EE | Kwang In Kim,
Matthias O. Franz,
Bernhard Schölkopf:
Iterative Kernel Principal Component Analysis for Image Modeling.
IEEE Trans. Pattern Anal. Mach. Intell. 27(9): 1351-1366 (2005) |
| 86 | EE | Matthias Hein,
Olivier Bousquet,
Bernhard Schölkopf:
Maximal margin classification for metric spaces.
J. Comput. Syst. Sci. 71(3): 333-359 (2005) |
| 85 | EE | Arthur Gretton,
Ralf Herbrich,
Alexander J. Smola,
Olivier Bousquet,
Bernhard Schölkopf:
Kernel Methods for Measuring Independence.
Journal of Machine Learning Research 6: 2075-2129 (2005) |
| 84 | EE | Athanassia Chalimourda,
Bernhard Schölkopf,
Alex J. Smola:
Experimentally optimal nu in support vector regression for different noise models and parameter settings.
Neural Networks 18(2): 205- (2005) |
| 2004 |
|---|
| 83 | | Sebastian Thrun,
Lawrence K. Saul,
Bernhard Schölkopf:
Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]
MIT Press 2004 |
| 82 | | Carl Edward Rasmussen,
Heinrich H. Bülthoff,
Bernhard Schölkopf,
Martin A. Giese:
Pattern Recognition, 26th DAGM Symposium, August 30 - September 1, 2004, Tübingen, Germany, Proceedings
Springer 2004 |
| 81 | EE | Matthias O. Franz,
Younghee Kwon,
Carl Edward Rasmussen,
Bernhard Schölkopf:
Semi-supervised Kernel Regression Using Whitened Function Classes.
DAGM-Symposium 2004: 18-26 |
| 80 | EE | Dengyong Zhou,
Bernhard Schölkopf:
Learning from Labeled and Unlabeled Data Using Random Walks.
DAGM-Symposium 2004: 237-244 |
| 79 | EE | Gökhan H. Bakir,
Arthur Gretton,
Matthias O. Franz,
Bernhard Schölkopf:
Multivariate Regression via Stiefel Manifold Constraints.
DAGM-Symposium 2004: 262-269 |
| 78 | EE | Wolf Kienzle,
Gökhan H. Bakir,
Matthias O. Franz,
Bernhard Schölkopf:
Efficient Approximations for Support Vector Machines in Object Detection.
DAGM-Symposium 2004: 54-61 |
| 77 | EE | Jihun Ham,
Daniel D. Lee,
Sebastian Mika,
Bernhard Schölkopf:
A kernel view of the dimensionality reduction of manifolds.
ICML 2004 |
| 76 | EE | N. Jeremy Hill,
Thomas Navin Lal,
Karin Bierig,
Niels Birbaumer,
Bernhard Schölkopf:
An Auditory Paradigm for Brain-Computer Interfaces.
NIPS 2004 |
| 75 | EE | Wolf Kienzle,
Gökhan H. Bakir,
Matthias O. Franz,
Bernhard Schölkopf:
Face Detection - Efficient and Rank Deficient.
NIPS 2004 |
| 74 | EE | Matthias O. Franz,
Bernhard Schölkopf:
Implicit Wiener Series for Higher-Order Image Analysis.
NIPS 2004 |
| 73 | EE | Bernhard Schölkopf,
Joachim Giesen,
Simon Spalinger:
Kernel Methods for Implicit Surface Modeling.
NIPS 2004 |
| 72 | EE | Felix A. Wichmann,
Arnulf B. A. Graf,
Eero P. Simoncelli,
Heinrich H. Bülthoff,
Bernhard Schölkopf:
Machine Learning Applied to Perception: Decision Images for Gender Classification.
NIPS 2004 |
| 71 | EE | Thomas Navin Lal,
Thilo Hinterberger,
Guido Widman,
Michael Schröder,
N. Jeremy Hill,
Wolfgang Rosenstiel,
Christian Erich Elger,
Bernhard Schölkopf,
Niels Birbaumer:
Methods Towards Invasive Human Brain Computer Interfaces.
NIPS 2004 |
| 70 | EE | Dengyong Zhou,
Bernhard Schölkopf,
Thomas Hofmann:
Semi-supervised Learning on Directed Graphs.
NIPS 2004 |
| 69 | EE | Holger Fröhlich,
Olivier Chapelle,
Bernhard Schölkopf:
Feature Selection for Support Vector Machines Using Genetic Algorithms.
International Journal on Artificial Intelligence Tools 13(4): 791-800 (2004) |
| 68 | EE | Ulrike von Luxburg,
Olivier Bousquet,
Bernhard Schölkopf:
A Compression Approach to Support Vector Model Selection.
Journal of Machine Learning Research 5: 293-323 (2004) |
| 67 | EE | Athanassia Chalimourda,
Bernhard Schölkopf,
Alex J. Smola:
Experimentally optimal v in support vector regression for different noise models and parameter settings.
Neural Networks 17(1): 127-141 (2004) |
| 66 | EE | Alexander J. Smola,
Bernhard Schölkopf:
A tutorial on support vector regression.
Statistics and Computing 14(3): 199-222 (2004) |
| 2003 |
|---|
| 65 | | Bernhard Schölkopf,
Manfred K. Warmuth:
Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings
Springer 2003 |
| 64 | EE | Holger Fröhlich,
Olivier Chapelle,
Bernhard Schölkopf:
Feature Selection for Support Vector Machines by Means of Genetic Algorithms.
ICTAI 2003: 142-148 |
| 63 | EE | Gökhan H. Bakir,
Jason Weston,
Bernhard Schölkopf:
Learning to Find Pre-Images.
NIPS 2003 |
| 62 | EE | Dengyong Zhou,
Olivier Bousquet,
Thomas Navin Lal,
Jason Weston,
Bernhard Schölkopf:
Learning with Local and Global Consistency.
NIPS 2003 |
| 61 | EE | Jan Eichhorn,
Andreas S. Tolias,
Alexander Zien,
Malte Kuss,
Carl Edward Rasmussen,
Jason Weston,
Nikos Logothetis,
Bernhard Schölkopf:
Prediction on Spike Data Using Kernel Algorithms.
NIPS 2003 |
| 60 | EE | Dengyong Zhou,
Jason Weston,
Arthur Gretton,
Olivier Bousquet,
Bernhard Schölkopf:
Ranking on Data Manifolds.
NIPS 2003 |
| 59 | | Jason Weston,
Fernando Pérez-Cruz,
Olivier Bousquet,
Olivier Chapelle,
André Elisseeff,
Bernhard Schölkopf:
Feature selection and transduction for prediction of molecular bioactivity for drug design.
Bioinformatics 19(6): 764-771 (2003) |
| 58 | EE | Bernhard Schölkopf:
Statistical learning theory, capacity, and complexity.
Complexity 8(4): 87-94 (2003) |
| 57 | EE | Sebastian Mika,
Gunnar Rätsch,
Jason Weston,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller:
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces.
IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003) |
| 56 | EE | Jason Weston,
André Elisseeff,
Bernhard Schölkopf,
Michael E. Tipping:
Use of the Zero-Norm with Linear Models and Kernel Methods.
Journal of Machine Learning Research 3: 1439-1461 (2003) |
| 2002 |
|---|
| 55 | EE | Bernhard Schölkopf,
Jason Weston,
Eleazar Eskin,
Christina S. Leslie,
William Stafford Noble:
A Kernel Approach for Learning from almost Orthogonal Patterns.
ECML 2002: 511-528 |
| 54 | EE | Bernhard Schölkopf,
Alex J. Smola:
A Short Introduction to Learning with Kernels.
Machine Learning Summer School 2002: 41-64 |
| 53 | EE | Alex J. Smola,
Bernhard Schölkopf:
Bayesian Kernel Methods.
Machine Learning Summer School 2002: 65-117 |
| 52 | EE | Olivier Chapelle,
Jason Weston,
Bernhard Schölkopf:
Cluster Kernels for Semi-Supervised Learning.
NIPS 2002: 585-592 |
| 51 | EE | Jason Weston,
Olivier Chapelle,
André Elisseeff,
Bernhard Schölkopf,
Vladimir Vapnik:
Kernel Dependency Estimation.
NIPS 2002: 873-880 |
| 50 | EE | Bernhard Schölkopf,
Jason Weston,
Eleazar Eskin,
Christina S. Leslie,
William Stafford Noble:
A Kernel Approach for Learning from Almost Orthogonal Patterns.
PKDD 2002: 494-511 |
| 49 | | Nello Cristianini,
Bernhard Schölkopf:
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines.
AI Magazine 23(3): 31-42 (2002) |
| 48 | EE | Gunnar Rätsch,
Sebastian Mika,
Bernhard Schölkopf,
Klaus-Robert Müller:
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification.
IEEE Trans. Pattern Anal. Mach. Intell. 24(9): 1184-1199 (2002) |
| 47 | | Dennis DeCoste,
Bernhard Schölkopf:
Training Invariant Support Vector Machines.
Machine Learning 46(1-3): 161-190 (2002) |
| 2001 |
|---|
| 46 | EE | Bernhard Schölkopf,
Ralf Herbrich,
Alex J. Smola:
A Generalized Representer Theorem.
COLT/EuroCOLT 2001: 416-426 |
| 45 | | Stan Z. Li,
QingDong Fu,
Lie Gu,
Bernhard Schölkopf,
Yimin Cheng,
HongJiang Zhang:
Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation.
ICCV 2001: 674-679 |
| 44 | | Sami Romdhani,
Philip H. S. Torr,
Bernhard Schölkopf,
Andrew Blake:
Computationally Efficient Face Detection.
ICCV 2001: 695-700 |
| 43 | | Neil D. Lawrence,
Bernhard Schölkopf:
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise.
ICML 2001: 306-313 |
| 42 | EE | Dimitris Achlioptas,
Frank McSherry,
Bernhard Schölkopf:
Sampling Techniques for Kernel Methods.
NIPS 2001: 335-342 |
| 41 | EE | Olivier Chapelle,
Bernhard Schölkopf:
Incorporating Invariances in Non-Linear Support Vector Machines.
NIPS 2001: 609-616 |
| 40 | | Robert C. Williamson,
Alex J. Smola,
Bernhard Schölkopf:
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators.
IEEE Transactions on Information Theory 47(6): 2516-2532 (2001) |
| 39 | EE | Alex J. Smola,
Sebastian Mika,
Bernhard Schölkopf,
Robert C. Williamson:
Regularized Principal Manifolds.
Journal of Machine Learning Research 1: 179-209 (2001) |
| 38 | | Bernhard Schölkopf,
John C. Platt,
John Shawe-Taylor,
Alex J. Smola,
Robert C. Williamson:
Estimating the Support of a High-Dimensional Distribution.
Neural Computation 13(7): 1443-1471 (2001) |
| 2000 |
|---|
| 37 | | Robert C. Williamson,
Alex J. Smola,
Bernhard Schölkopf:
Entropy Numbers of Linear Function Classes.
COLT 2000: 309-319 |
| 36 | | Alex J. Smola,
Bernhard Schölkopf:
Sparse Greedy Matrix Approximation for Machine Learning.
ICML 2000: 911-918 |
| 35 | EE | Athanassia Chalimourda,
Bernhard Schölkopf,
Alex J. Smola:
Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments.
IJCNN (5) 2000: 199-204 |
| 34 | | Bernhard Schölkopf:
The Kernel Trick for Distances.
NIPS 2000: 301-307 |
| 33 | | Susanne Still,
Bernhard Schölkopf,
Klaus Hepp,
Rodney J. Douglas:
Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm.
NIPS 2000: 741-747 |
| 32 | | Paul Hayton,
Bernhard Schölkopf,
Lionel Tarassenko,
Paul Anuzis:
Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra.
NIPS 2000: 946-952 |
| 31 | | Gunnar Rätsch,
Bernhard Schölkopf,
Alex J. Smola,
Sebastian Mika,
Takashi Onoda,
Klaus-Robert Müller:
Robust Ensemble Learning for Data Mining.
PAKDD 2000: 341-344 |
| 30 | | Alexander Zien,
Gunnar Rätsch,
Sebastian Mika,
Bernhard Schölkopf,
Thomas Lengauer,
Klaus-Robert Müller:
Engineering support vector machine kernels that recognize translation initiation sites.
Bioinformatics 16(9): 799-807 (2000) |
| 29 | | Bernhard Schölkopf,
Alex J. Smola,
Robert C. Williamson,
Peter L. Bartlett:
New Support Vector Algorithms.
Neural Computation 12(5): 1207-1245 (2000) |
| 1999 |
|---|
| 28 | EE | Alex J. Smola,
Robert C. Williamson,
Sebastian Mika,
Bernhard Schölkopf:
Regularized Principal Manifolds.
EuroCOLT 1999: 214-229 |
| 27 | EE | Robert C. Williamson,
Alex J. Smola,
Bernhard Schölkopf:
Entropy Numbers, Operators and Support Vector Kernels.
EuroCOLT 1999: 285-299 |
| 26 | | Alexander Zien,
Gunnar Rätsch,
Sebastian Mika,
Bernhard Schölkopf,
Christian Lemmen,
Alex J. Smola,
Thomas Lengauer,
Klaus-Robert Müller:
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites.
German Conference on Bioinformatics 1999: 37-43 |
| 25 | EE | Alex J. Smola,
John Shawe-Taylor,
Bernhard Schölkopf,
Robert C. Williamson:
The Entropy Regularization Information Criterion.
NIPS 1999: 342-348 |
| 24 | EE | Sebastian Mika,
Gunnar Rätsch,
Jason Weston,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller:
Invariant Feature Extraction and Classification in Kernel Spaces.
NIPS 1999: 526-532 |
| 23 | EE | Gunnar Rätsch,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller,
Takashi Onoda,
Sebastian Mika:
v-Arc: Ensemble Learning in the Presence of Outliers.
NIPS 1999: 561-567 |
| 22 | EE | Bernhard Schölkopf,
Robert C. Williamson,
Alex J. Smola,
John Shawe-Taylor,
John C. Platt:
Support Vector Method for Novelty Detection.
NIPS 1999: 582-588 |
| 21 | EE | Bernhard Schölkopf,
Sebastian Mika,
Christopher J. C. Burges,
Phil Knirsch,
Klaus-Robert Müller,
Gunnar Rätsch,
Alexander J. Smola:
Input space versus feature space in kernel-based methods.
IEEE Transactions on Neural Networks 10(5): 1000-1017 (1999) |
| 20 | EE | Bernhard Schölkopf,
Klaus-Robert Müller,
Alex J. Smola:
Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten.
Inform., Forsch. Entwickl. 14(3): 154-163 (1999) |
| 1998 |
|---|
| 19 | | Bernhard Schölkopf,
Alex J. Smola,
Phil Knirsch,
Chris Burges:
Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces.
DAGM-Symposium 1998: 125-132 |
| 18 | | Matthias O. Franz,
Bernhard Schölkopf,
Hanspeter A. Mallot,
Heinrich H. Bülthoff,
Andreas Zell:
Navigation mit Schnappschüssen.
DAGM-Symposium 1998: 421-428 |
| 17 | EE | Bernhard Schölkopf,
Peter L. Bartlett,
Alex J. Smola,
Robert C. Williamson:
Shrinking the Tube: A New Support Vector Regression Algorithm.
NIPS 1998: 330-336 |
| 16 | EE | Sebastian Mika,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller,
Matthias Scholz,
Gunnar Rätsch:
Kernel PCA and De-Noising in Feature Spaces.
NIPS 1998: 536-542 |
| 15 | EE | Alex J. Smola,
Thilo-Thomas Frieß,
Bernhard Schölkopf:
Semiparametric Support Vector and Linear Programming Machines.
NIPS 1998: 585-591 |
| 14 | | Alex J. Smola,
Bernhard Schölkopf:
On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion.
Algorithmica 22(1/2): 211-231 (1998) |
| 13 | | Matthias O. Franz,
Bernhard Schölkopf,
Hanspeter A. Mallot,
Heinrich H. Bülthoff:
Learning View Graphs for Robot Navigation.
Auton. Robots 5(1): 111-125 (1998) |
| 12 | | Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller:
Nonlinear Component Analysis as a Kernel Eigenvalue Problem.
Neural Computation 10(5): 1299-1319 (1998) |
| 11 | EE | Alex J. Smola,
Bernhard Schölkopf,
Klaus-Robert Müller:
The connection between regularization operators and support vector kernels.
Neural Networks 11(4): 637-649 (1998) |
| 1997 |
|---|
| 10 | | Matthias O. Franz,
Bernhard Schölkopf,
Philip Georg,
Hanspeter A. Mallot,
Heinrich H. Bülthoff:
Learning View Graphs for Robot Navigation.
Agents 1997: 138-147 |
| 9 | | Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller:
Kernel Principal Component Analysis.
ICANN 1997: 583-588 |
| 8 | | Hanspeter A. Mallot,
Matthias O. Franz,
Bernhard Schölkopf,
Heinrich H. Bülthoff:
The View-Graph Approach to Visual Navigation and Spatial Memory.
ICANN 1997: 751-756 |
| 7 | | Klaus-Robert Müller,
Alex J. Smola,
Gunnar Rätsch,
Bernhard Schölkopf,
Jens Kohlmorgen,
Vladimir Vapnik:
Predicting Time Series with Support Vector Machines.
ICANN 1997: 999-1004 |
| 6 | | Alex J. Smola,
Bernhard Schölkopf:
From Regularization Operators to Support Vector Kernels.
NIPS 1997 |
| 5 | | Bernhard Schölkopf,
Patrice Simard,
Alex J. Smola,
Vladimir Vapnik:
Prior Knowledge in Support Vector Kernels.
NIPS 1997 |
| 1996 |
|---|
| 4 | | Volker Blanz,
Bernhard Schölkopf,
Heinrich H. Bülthoff,
Chris Burges,
Vladimir Vapnik,
Thomas Vetter:
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models.
ICANN 1996: 251-256 |
| 3 | | Bernhard Schölkopf,
Chris Burges,
Vladimir Vapnik:
Incorporating Invariances in Support Vector Learning Machines.
ICANN 1996: 47-52 |
| 2 | EE | Christopher J. C. Burges,
Bernhard Schölkopf:
Improving the Accuracy and Speed of Support Vector Machines.
NIPS 1996: 375-381 |
| 1995 |
|---|
| 1 | | Bernhard Schölkopf,
Chris Burges,
Vladimir Vapnik:
Extracting Support Data for a Given Task.
KDD 1995: 252-257 |
