S
Sören Sonnenburg
Researcher at Max Planck Society
Publications - 33
Citations - 5002
Sören Sonnenburg is an academic researcher from Max Planck Society. The author has contributed to research in topics: Support vector machine & Multiple kernel learning. The author has an hindex of 24, co-authored 33 publications receiving 4788 citations. Previous affiliations of Sören Sonnenburg include Technical University of Berlin & University of Potsdam.
Papers
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Journal Article
Large Scale Multiple Kernel Learning
TL;DR: It is shown that the proposed multiple kernel learning algorithm can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations, and generalize the formulation and the method to a larger class of problems, including regression and one-class classification.
Journal ArticleDOI
Support vector machines and kernels for computational biology.
TL;DR: Support vector machines are widely used in computational biology due to their high accuracy, their ability to deal with high-dimensional and large datasets, and their flexibility in modeling diverse sources of data.
Journal ArticleDOI
l p -Norm Multiple Kernel Learning
TL;DR: Empirical applications of lp-norm MKL to three real-world problems from computational biology show that non-sparse MKL achieves accuracies that surpass the state-of-the-art, and two efficient interleaved optimization strategies for arbitrary norms are developed.
Journal ArticleDOI
The SHOGUN Machine Learning Toolbox
Sören Sonnenburg,Gunnar Rätsch,Sebastian Henschel,Christian Widmer,Jonas Behr,Alexander Zien,Fabio De Bona,Alexander Binder,Christian Gehl,Vojtěch Franc +9 more
TL;DR: A machine learning toolbox designed for unified large-scale learning for a broad range of feature types and learning settings, which offers a considerable number of machine learning models such as support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis, and more.
Proceedings Article
Efficient and Accurate Lp-Norm Multiple Kernel Learning
TL;DR: This work devise new insights on the connection between several existing MKL formulations and develop two efficient interleaved optimization strategies for arbitrary p > 1 and applies lp-norm MKL to real-world problems from computational biology, showing that non-sparse MKL achieves accuracies that go beyond the state-of-the-art.