Bernhard Schölkopf's scientific interests are in the field of machine learning and perception. In particular, he studies support vector and kernel methods for understanding high-dimensional data.
He is on the editorial boards of:
- Journal of Machine Learning Research, an online journal which he helped launch as a founding action editor in early 2000. JMLR is the flagship journal of machine learning.
- International Journal of Computer Vision, one of the two flagship journals of computer vision (with IEEE PAMI, see below)
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Information Science and Statistics, a Springer series of monographs
- Advances in Data Analysis and Classification (founding member)
- SIAM Imaging Sciences (founding member)
- Foundations and Trends in Machine Learning (founding member)
With 5-year impact factors (ISI, 2008) of 10.3 and 8.0, respectively, IJCV and PAMI are the two top journals in the general area of artificial intelligence (they are ranked three and five in all of computer science). JMLR is number four (5.9).
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lecture
Kernel Methods
as author at Machine Learning Summer School (MLSS), La Palma 2012,
643 views
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tutorial
Kernel Methods
as author at Machine Learning Summer School (MLSS ), Bordeaux 2011,
2197 views
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lecture
Introduction to kernel methods
as author at Machine Learning Summer School (MLSS), Tübingen 2007,
7404 views
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lecture
Learning with Kernels
as author at Machine Learning Summer School (MLSS), Tübingen 2003,
5354 views
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tutorial
Kernel Methods
as author at Machine Learning Summer School (MLSS), Cambridge 2009,
2230 views
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lecture
Introduction to Kernel Methods
as author at Machine Learning Summer School (MLSS), Berder Island 2004,
3211 views
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keynote
From kernels to causal inference
as author at 25th Annual Conference on Neural Information Processing Systems (NIPS), Granada 2011,
639 views
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invited talk
Kernel Tricks, Means and Ends
as author at 24th Annual International Conference on Machine Learning (ICML), Corvallis 2007,
2416 views
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tutorial
Machine learning for cognitive science 2: Bayesian methods and statistical learning theory
as author at Cognitive Science and Machine Learning Summer School (MLSS), Sardinia 2010,
1042 views
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lecture
Learning with Kernels
as author at Machine Learning Summer School (MLSS), Canberra 2006,
1332 views
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lecture
Support Vector Machines and Kernels
as author at Machine Learning Summer School (MLSS), Canberra 2002,
1168 views
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invited talk
From Injective Hilbert Space Embeddings to Deconvolution
as author at Machine Learning meets Computational Photography,
339 views
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lecture
A discussion about ML
as author at Machine Learning Summer School (MLSS), Tübingen 2007,
705 views
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tutorial
Machine learning for cognitive science 3: Kernel methods and Bayesian methods
as author at Cognitive Science and Machine Learning Summer School (MLSS), Sardinia 2010,
358 views
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interview
Interview about past, present, future of MLSS
as author at Machine Learning Summer School (MLSS), Tübingen 2007,
665 views
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panel
Opportunities for cosmology to meet machine learning
as author at Cosmology meets Machine Learning,
together with:
Iain Murray,
Robert Lupton,
Jean-Luc Starck,
Alexandre Refregier,
David W. Hogg,
Neil D. Lawrence,
Rob Fergus,
Phil Marshall (moderator),
154 views
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lecture
Opening of the 9th Machine Learning Summer School
as author at Machine Learning Summer School (MLSS), Tübingen 2007,
635 views
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lecture
Kernel Methods
as author at The Analysis of Patterns, Erice 2005,
183 views
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lecture
Closing remarks
as author at Machine Learning Summer School (MLSS), Tübingen 2007,
134 views
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lecture
Object Correspondence as a Machine Learning Problem
as author at NIPS Workshop on Kernel Methods and Structured Domains / NIPS Workshop on Large Scale Kernel Machines, Whistler 2005,
128 views
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Springer,
