S. Sonnenburg

发表

l p -Norm Multiple Kernel Learning

M. Kloft, Ulf Brefeld, S. Sonnenburg, 2011 .

The SHOGUN Machine Learning Toolbox

Gunnar Rätsch, Alexander Binder, Alexander Zien, 2010, J. Mach. Learn. Res..

Large Scale Learning with String Kernels

K. Rieck, S. Sonnenburg, G. Rätsch, 2006 .

mGene: accurate SVM-based gene finding with an application to nematode genomes.

Cheng Soon Ong, S. Sonnenburg, A. Zien, 2009, Genome research.

The Need for Open Source Software in Machine Learning

Carl E. Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, 2007, J. Mach. Learn. Res..

Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees

Sören Sonnenburg, Konrad Rieck, Pavel Laskov, 2006, NIPS.

Non-sparse Multiple Kernel Learning

M. Kloft, Ulf Brefeld, S. Sonnenburg, 2008 .

Efficient and Accurate Lp-Norm Multiple Kernel Learning

Klaus-Robert Müller, Alexander Zien, Sören Sonnenburg, 2009, NIPS.

Large Scale Multiple Kernel Learning

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2006, J. Mach. Learn. Res..

COFFIN: A Computational Framework for Linear SVMs

Sören Sonnenburg, Vojtech Franc, S. Sonnenburg, 2010, ICML.

Positional Oligomer Importance Matrices

S. Sonnenburg, A. Zien, G. Rätsch, 2007, NIPS 2007.

POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors

Gunnar Rätsch, Alexander Zien, Sören Sonnenburg, 2008, ISMB.

Support Vector Machines and Kernels for Computational Biology

Gunnar Rätsch, Bernhard Schölkopf, Cheng Soon Ong, 2008, PLoS Comput. Biol..

Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization

Sören Sonnenburg, Vojtech Franc, S. Sonnenburg, 2009, J. Mach. Learn. Res..

ARTS: accurate recognition of transcription starts in human

Gunnar Rätsch, Alexander Zien, Sören Sonnenburg, 2006, ISMB.

Large Scale Hidden Semi-Markov SVMs

Gunnar Rätsch, Sören Sonnenburg, S. Sonnenburg, 2006, NIPS.

A New Discriminative Kernel from Probabilistic Models

Gunnar Rätsch, Motoaki Kawanabe, Klaus-Robert Müller, 2001, Neural Computation.

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2006, PLoS Comput. Biol..

lp-Norm Multiple Kernel Learning

Alexander Zien, Sören Sonnenburg, Marius Kloft, 2011, J. Mach. Learn. Res..

Classifying 'Drug-likeness' with Kernel-Based Learning Methods

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2005, J. Chem. Inf. Model..

nematode genomes mGene : Accurate SVM-based gene finding with an application to Material Supplemental

Cheng Soon Ong, S. Sonnenburg, A. Zien, 2009 .

Accurate prediction of alternative splicing events

Gunnar Rätsch, Bernhard Schölkopf, Cheng Soon Ong, 2005, NIPS 2005.

mTIM: A margin-based transcript identification method

S. Sonnenburg, G. Rätsch, Jonas Behr, 2011 .

Non-Sparse Regularization and Efficient Training with Multiple Kernels pdf

Alexander Zien, Sören Sonnenburg, Marius Kloft, 2010, ArXiv.

Learning Interpretable SVMs for Biological Sequence Classification

Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer, 2005, BMC Bioinformatics.

Machine learning for genomic sequence analysis

Sören Sonnenburg, S. Sonnenburg, 2008 .

mGene.web: a web service for accurate computational gene finding

Gunnar Rätsch, Cheng Soon Ong, Alexander Zien, 2009, Nucleic Acids Res..

Accurate splice site prediction using support vector machines

Gunnar Rätsch, Sören Sonnenburg, Petra Philips, 2007, BMC Bioinformatics.

Large scale genomic sequence SVM classifiers

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2005, ICML.

Nanotechnology in the Environmental Health Sciences

B. Schölkopf, K. Müller, S. Sonnenburg, 2005, Environmental Health Perspectives.

RASE: recognition of alternatively spliced exons in C.elegans

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2005, ISMB.

Non-Sparse Regularization with Multiple Kernels

Alexander Zien, Sören Sonnenburg, Marius Kloft, 2010 .

A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines

Robert Jenssen, Klaus-Robert Müller, Alexander Zien, 2012, PloS one.

A new scatter-based multi-class support vector machine

Robert Jenssen, Klaus-Robert Müller, Alexander Zien, 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

A General and Efficient Multiple Kernel Learning Algorithm

Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer, 2005, NIPS.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

Gilles Blanchard, Sören Sonnenburg, Arcadi Navarro, 2016, Scientific Reports.

The Feature Importance Ranking Measure

Gunnar Rätsch, Alexander Zien, Nicole Krämer, 2009, ECML/PKDD.

Optimized cutting plane algorithm for support vector machines

Sören Sonnenburg, Vojtěch Franc, S. Sonnenburg, 2008, ICML '08.

The SHOGUN Machine Learning Toolbox

S. Sonnenburg, A. Zien, G. Rätsch, 2010, J. Mach. Learn. Res..

Non-sparse Multiple Kernel Learning

M. Kloft, Ulf Brefeld, S. Sonnenburg, 2008 .

New Methods for Splice Site Recognition

Gunnar Rätsch, Klaus-Robert Müller, Sören Sonnenburg, 2002, ICANN.

13 Accurate Splice Site Detection for Caenorhabditis elegans

Gunnar Rätsch, Sören Sonnenburg, S. Sonnenburg, 2004 .

POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors

S. Sonnenburg, A. Zien, G. Rätsch, 2008, ISMB.