Sören Sonnenburg

发表

The Feature Importance Ranking Measure

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

mGene: A Novel Discriminative Gene Finding System

Cheng Soon Ong, Alexander Zien, Sören Sonnenburg, 2007, ISMB 2007.

The Need for Open Source Software in Machine Learning

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

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

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

Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation

Gunnar Rätsch, André Kahles, Sören Sonnenburg, 2011, NIPS.

Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization

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

Accurate prediction of alternative splicing events

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

lp-Norm Multiple Kernel Learning

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

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 .

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 SHOGUN Machine Learning Toolbox

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

Optimized cutting plane algorithm for support vector machines

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

Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees

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

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

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

Comparing Sparse and Non-Sparse Multiple Kernel Learning

Alexander Zien, Sören Sonnenburg, Marius Kloft, 2009 .

Large Scale Multiple Kernel Learning

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

Non-Sparse Regularization with Multiple Kernels

Alexander Zien, Sören Sonnenburg, Marius Kloft, 2010 .

Accurate splice site prediction using support vector machines

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

ARTS: accurate recognition of transcription starts in human

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

Support Vector Machines and Kernels for Computational Biology

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

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.

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

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

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

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

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.

Non-Sparse Regularization and Efficient Training with Multiple Kernels pdf

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

Efficient and Accurate Lp-Norm Multiple Kernel Learning

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

RASE: recognition of alternatively spliced exons in C.elegans

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

New Methods for Splice Site Recognition

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

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

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

A General and Efficient Multiple Kernel Learning Algorithm

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

COFFIN: A Computational Framework for Linear SVMs

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

Learning Interpretable SVMs for Biological Sequence Classification

Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer, 2006, BMC Bioinformatics.

Large scale genomic sequence SVM classifiers

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

13 Accurate Splice Site Detection for Caenorhabditis elegans

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