Kernel Methods
We studied kernel methods and SVMs, in particular in the context of semi-supervised learning and data-dependent regularization.
Lab members are shown in this color.
2013
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F. H. Sinz, A. Stöckl, J. Grewe, J. Benda
Least Informative Dimensions
Advances in Neural Information Processing Systems 26, 413-421
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2007
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F. H. Sinz
A priori knowledge from non-examples
Diploma Thesis University Tübingen
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F. H. Sinz, O. Chapelle, A. Agarwal, B. Schölkopf
An Analysis of Inference with the Universum
Advances in Neural Information Processing Systems, 1-8
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2006
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F. Sinz, B. Schölkopf
Minimal Logical Constraint Covering Sets Minimal Logical Constraint Covering Sets
Technical Report Max Planck Institute for Biological Cybernetics
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R. Collobert, F. Sinz, J. Weston, L. Bottou
Large Scale Transductive SVMs
Journal of Machine Learning Research, 7, 1687-1712
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J. Weston, R. Collobert, F. Sinz, L. Bottou, V. Vapnik
Inference with the Universum
Proceedings of the 23rd international conference on Machine learning ICML 06, 1009-1016
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R. Collobert, F. Sinz, J. Weston, L. Bottou
Trading convexity for scalability
International Conference on Machine Learning, 201-208
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2004
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F.H. Sinz, J. Quinonero-Candela, G.H. Bakir, C.E. Rasmussen, M.O. Franz
Learning Depth From Stereo
Pattern Recognition, Proc. 26th DAGM Symposium, 3175, 1-8
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