2009 | ||
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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 |