61 results
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**Maschinelles Lernen: Entwicklung ohne Grenzen?**
In *Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen*, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

**Methods in Psychophysics**
In *Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience*, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

**Transfer Learning for BCIs**
In *Brain–Computer Interfaces Handbook*, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

**Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI**
Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience},
year = {2013},
month = {7},
volume = {14},
number = {Supplement 1},
pages = {A1}, (talk)

**A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)**
In *Brain-Computer Interface Research*, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

**Semi-supervised learning in causal and anticausal settings**
In *Empirical Inference*, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

**Tractable large-scale optimization in machine learning**
In *Tractability: Practical Approaches to Hard Problems*, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

**MR-Based Attenuation Correction for Combined Brain PET/MR: Robustness of Atlas- and Pattern Recognition Method to Atlas Registration Failures**
IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE MIC), 2013 (talk)

**Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24 **
pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

**Domain Generalization via Invariant Feature Representation**
30th International Conference on Machine Learning (ICML2013), 2013 (talk)

**On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension**
In *Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik*, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

Zhou, D.
**How to learn from very few examples?**
October 2004 (talk)

Zhou, D.
**Discrete vs. Continuous: Two Sides of Machine Learning**
October 2004 (talk)

Zhou, D.
**Discrete vs. Continuous: Two Sides of Machine Learning**
October 2004 (talk)

**Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung**
September 2004 (talk)

**Advanced Lectures on Machine Learning**
*ML Summer Schools 2003*, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

**Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175**
*Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04)*, pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

**The benefit of liquid Helium cooling for Cryo-Electron Tomography: A quantitative
comparative study**
The thirteenth European Microscopy Congress, August 2004 (talk)

**Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis**
In Volume 1: Cellular and Molecular Tools, pages: 109-128, (Editors: Sabatini, M., P. Pastoureau and F. De Ceuninck), Humana Press, July 2004 (inbook)

**Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference**
*Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)*, pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

**Distributed Command Execution**
In *BSD Hacks: 100 industrial-strength tips & tools*, pages: 152-152, (Editors: Lavigne, Dru), O’Reilly, Beijing, May 2004 (inbook)

Bousquet, O.
**Introduction to Category Theory**
Internal Seminar, January 2004 (talk)

**Gaussian Processes in Machine Learning**
In 3176, pages: 63-71, Lecture Notes in Computer Science, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, 2004, Copyright by Springer (inbook)

**Local Alignment Kernels for Biological Sequences**
In *Kernel Methods in Computational Biology*, pages: 131-153, MIT Press, Cambridge, MA,, 2004 (inbook)

**Protein Classification via Kernel Matrix Completion**
In pages: 261-274, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

**Introduction to Statistical Learning Theory**
In Lecture Notes in Artificial Intelligence 3176, pages: 169-207, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

**A Primer on Kernel Methods**
In *Kernel Methods in Computational Biology*, pages: 35-70, (Editors: B Schölkopf and K Tsuda and JP Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

**Concentration Inequalities**
In Lecture Notes in Artificial Intelligence 3176, pages: 208-240, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

**Kernels for graphs**
In pages: 155-170, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

**A primer on molecular biology**
In pages: 3-34, (Editors: Schoelkopf, B., K. Tsuda and J. P. Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

Bousquet, O.
**Advanced Statistical Learning Theory**
Machine Learning Summer School, 2004 (talk)

**Learning Control and Planning from the View of Control Theory and Imitation**
NIPS Workshop "Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (talk)

**Recurrent neural networks from learning attractor dynamics**
NIPS Workshop on RNNaissance: Recurrent Neural Networks, December 2003 (talk)

**Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), LNCS Vol. 2777**
*Proceedings of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003)*, *COLT/Kernel 2003*, pages: 746, Springer, Berlin, Germany, 16th Annual Conference on Learning Theory and 7th Kernel Workshop, November 2003, Lecture Notes in Computer Science ; 2777 (proceedings)

Bousquet, O.
**Statistical Learning Theory**
Machine Learning Summer School, August 2003 (talk)

**Remarks on Statistical Learning Theory**
Machine Learning Summer School, August 2003 (talk)

**Rademacher and Gaussian averages in Learning Theory**
Universite de Marne-la-Vallee, March 2003 (talk)

Bousquet, O., Schölkopf, B.
**Statistical Learning Theory**
March 2003 (talk)

**Concentration Inequalities and Data-Dependent Error Bounds**
Uni. Jena, February 2003 (talk)

**Introduction: Robots with Cognition?**
6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

**Extension of the nu-SVM range for classification**
In *Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190*, 190, pages: 179-196, NATO Science Series III: Computer and Systems Sciences, (Editors: J Suykens and G Horvath and S Basu and C Micchelli and J Vandewalle), IOS Press, Amsterdam, 2003 (inbook)

**Support Vector Machines**
In *Handbook of Brain Theory and Neural Networks (2nd edition)*, pages: 1119-1125, (Editors: MA Arbib), MIT Press, Cambridge, MA, USA, 2003 (inbook)

**An Introduction to Support Vector Machines**
In *Recent Advances and Trends in Nonparametric Statistics
*, pages: 3-17, (Editors: MG Akritas and DN Politis), Elsevier, Amsterdam, The Netherlands, 2003 (inbook)

**Statistical Learning and Kernel Methods in Bioinformatics**
In *Artificial Intelligence and Heuristic Methods in Bioinformatics*, 183, pages: 1-21, 3, (Editors: P Frasconi und R Shamir), IOS Press, Amsterdam, The Netherlands, 2003 (inbook)

**A Short Introduction to Learning with Kernels**
In *Proceedings of the Machine Learning Summer School, Lecture Notes in Artificial Intelligence, Vol. 2600*, pages: 41-64, LNAI 2600, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

**Bayesian Kernel Methods**
In *Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Lecture Notes in Computer Science, Vol. 2600*, LNAI 2600, pages: 65-117, 0, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Germany, 2003 (inbook)