Soeren Sonnenburg
Soeren Sonnenburg
Machine Learning Researcher
Verified email at - Homepage
Cited by
Cited by
Large scale multiple kernel learning
S Sonnenburg, G Rätsch, C Schäfer, B Schölkopf
The Journal of Machine Learning Research 7, 1531-1565, 2006
Support vector machines and kernels for computational biology
A Ben-Hur, CS Ong, S Sonnenburg, B Schölkopf, G Rätsch
PLoS computational biology 4 (10), e1000173, 2008
lp-Norm Multiple Kernel Learning
M Kloft, U Brefeld, S Sonnenburg, A Zien
The Journal of Machine Learning Research 12, 953-997, 2011
The SHOGUN machine learning toolbox
S Sonnenburg, G Rätsch, S Henschel, C Widmer, J Behr, A Zien, F Bona, ...
The Journal of Machine Learning Research 11, 1799-1802, 2010
Efficient and accurate lp-norm multiple kernel learning
M Kloft, U Brefeld, P Laskov, KR Müller, A Zien, S Sonnenburg
Advances in neural information processing systems 22, 2009
The need for open source software in machine learning
S Sonnenburg, ML Braun, CS Ong, S Bengio, L Bottou, G Holmes, ...
Accurate splice site prediction using support vector machines
S Sonnenburg, G Schweikert, P Philips, J Behr, G Rätsch
BMC bioinformatics 8, 1-16, 2007
A general and efficient multiple kernel learning algorithm
S Sonnenburg, G Rätsch, C Schäfer
Advances in neural information processing systems 18, 2005
A new discriminative kernel from probabilistic models
K Tsuda, M Kawanabe, G Rätsch, S Sonnenburg, KR Müller
Advances in Neural Information Processing Systems 14, 2001
Optimized cutting plane algorithm for support vector machines
V Franc, S Sonnenburg
Proceedings of the 25th international conference on Machine learning, 320-327, 2008
RASE: recognition of alternatively spliced exons in C.elegans
G Rätsch, S Sonnenburg, B Schölkopf
Bioinformatics 21 (suppl_1), i369-i377, 2005
ARTS: accurate recognition of transcription starts in human
S Sonnenburg, A Zien, G Rätsch
Bioinformatics 22 (14), e472-e480, 2006
The feature importance ranking measure
A Zien, N Krämer, S Sonnenburg, G Rätsch
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2009
Non-sparse multiple kernel learning
M Kloft, U Brefeld, P Laskov, S Sonnenburg
NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels 4, 5, 2008
13 Accurate Splice Site Detection for Caenorhabditis elegans
G Rätsch, S Sonnenburg
Kernel methods in computational biology 277, 2004
Classifying ‘drug-likeness' with kernel-based learning methods
KR Müller, G Rätsch, S Sonnenburg, S Mika, M Grimm, N Heinrich
Journal of chemical information and modeling 45 (2), 249-253, 2005
mGene: accurate SVM-based gene finding with an application to nematode genomes
G Schweikert, A Zien, G Zeller, J Behr, C Dieterich, CS Ong, P Philips, ...
Genome research 19 (11), 2133-2143, 2009
Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning
G Rätsch, S Sonnenburg, J Srinivasan, H Witte, KR Müller, RJ Sommer, ...
PLoS Computational Biology 3 (2), e20, 2007
Learning interpretable SVMs for biological sequence classification
G Rätsch, S Sonnenburg, C Schäfer
BMC bioinformatics 7, 1-14, 2006
New methods for splice site recognition
S Sonnenburg, G Rätsch, A Jagota, KR Müller
Artificial Neural Networks—ICANN 2002: International Conference Madrid …, 2002
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