Kernel Methods

past research
kernel methods
Kernel Methods

We studied kernel methods and SVMs, in particular in the context of semi-supervised learning and data-dependent regularization.


Publications

Lab members are shown in this color. Preprints are shown in this color.


Oct 2018 - Neuronal Intelligence Lab Start

2013

F. H. Sinz, A. Stöckl, J. Grewe, J. Benda Least Informative Dimensions Advances in Neural Information Processing Systems 26, 413-421

2007

F. H. Sinz A priori knowledge from non-examples Diploma Thesis University Tübingen
F. H. Sinz, O. Chapelle, A. Agarwal, B. Schölkopf An Analysis of Inference with the Universum Advances in Neural Information Processing Systems, 1-8

2006

F. Sinz, B. Schölkopf Minimal Logical Constraint Covering Sets Minimal Logical Constraint Covering Sets Technical Report Max Planck Institute for Biological Cybernetics
R. Collobert, F. Sinz, J. Weston, L. Bottou Large Scale Transductive SVMs Journal of Machine Learning Research, 7, 1687-1712
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
R. Collobert, F. Sinz, J. Weston, L. Bottou Trading convexity for scalability International Conference on Machine Learning, 201-208

2004

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