Publications of G Rätsch

Sonnenburg, S.; Rätsch, G.; Henschel, S.; Widmer, C.; Behr, J.; Zien, A.; De Bona, F.; Binder, A.; Gehl, C.; Franc, V.: The SHOGUN Machine Learning Toolbox. Journal of Machine Learning Research 11, pp. 1799 - 1802 (2010)

Stegle, O.; Drewe, P.; Bohnert, R.; Borgwardt, K.; Rätsch, G.: Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts. Nature Precedings 2010, pp. 1 - 11 (2010)

Schweikert, G.; Zeller, G.; Behr, J.; Dieterich, C.; Ong, C.; Philips, P.; De Bona, F.; Hartmann, L.; Bohlen, A.; Krüger, N. et al.; Sonnenburg, S.; Rätsch, G.: mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research 19 (11), pp. 2133 - 2143 (2009)

Schweikert, G.; Behr, J.; Zien, A.; Zeller, G.; Ong, C.; Sonnenburg, S.; Rätsch, G.: mGene.web: a web service for accurate computational gene finding. Nucleic Acids Research (London) 37 (Supplement 2), pp. W312 - W316 (2009)

Graf, A.; Bousquet, O.; Rätsch, G.; Schölkopf, B.: Prototype Classification: Insights from Machine Learning. Neural computation 21 (1), pp. 272 - 300 (2009)

Ben-Hur, A.; Ong, C.; Sonnenburg, S.; Schölkopf, B.; Rätsch, G.: Support Vector Machines and Kernels for Computational Biology. PLoS Computational Biology 4 (10), e1000173 (2008)

Laubinger, S.; Zeller, G.; Henz, S.; Sachsenberg, T.; Widmer, C.; Naouar, N.; Vuylsteke, M.; Schölkopf, B.; Rätsch, G.; Weigel, D.: At-TAX: A Whole Genome Tiling Array Resource for Developmental Expression Analysis and Transcript Identification in Arabidopsis thaliana. Genome Biology 9 (7), R112, pp. 1 - 16 (2008)

Sonnenburg, S.; Braun, M.; Ong, C.; Bengio, S.; Bottou, L.; Holmes , G.; LeCun, Y.; Müller, K.-R.; Pereira, F.; Rasmussen, C. et al.; Rätsch, G.; Schölkopf, B.; Smola, A.; Vincent, P.; Weston, J.; Williamson, R.: The Need for Open Source Software in Machine Learning. The Journal of Machine Learning Research 8, pp. 2443 - 2466 (2007)

Clark, R.; Schweikert, G.; Toomajian, C.; Ossowski , S.; Zeller, G.; Shinn, P.; Warthmann, N.; Hu, T.; Fu, G.; Hinds, D. et al.; Chen, H.; Frazer, K.; Huson, D.; Schölkopf, B.; Nordborg, M.; Rätsch, G.; Ecker, J.; Weigel, D.: Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana. Science 317 (5836), pp. 338 - 342 (2007)

Schulze, U.; Hepp, B.; Ong, C.; Rätsch, G.: PALMA: mRNA to Genome Alignments using Large Margin Algorithms. Bioinformatics 23 (15), pp. 1892 - 1900 (2007)

Rätsch, G.; Sonnenburg, S.; Srinivasan, J.; Witte, H.; Müller, K.-R.; Sommer, R.-J.; Schölkopf, B.: Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Computational Biology 3 (2 ), pp. 0313 - 0322 (2007)

Sonnenburg, S.; Rätsch, G.; Schäfer, C.; Schölkopf, B.: Large Scale Multiple Kernel Learning. The Journal of Machine Learning Research 7, pp. 1531 - 1565 (2006)

Tsuda, K.; Rätsch, G.: Image Reconstruction by Linear Programming. IEEE Transactions on Image Processing 14 (6), pp. 737 - 744 (2005)

Tsuda, K.; Rätsch, G.; Warmuth , M.: Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. The Journal of Machine Learning Research 6, pp. 995 - 1018 (2005)

Mika, S.; Rätsch, G.; Weston, J.; Schölkopf, B.; Smola, A.; Müller, K.-R.: Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (5), pp. 623 - 628 (2003)

Tsuda, K.; Kawanabe, M.; Rätsch, G.; Sonnenburg, S.; Müller, K.: A New Discriminative Kernel from Probabilistic Models. Neural computation 14 (10), pp. 2397 - 2414 (2002)

Rätsch, G.; Mika, S.; Schölkopf, B.; Müller, K.-R.: Constructing Boosting algorithms from SVMs: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (9), pp. 1184 - 1199 (2002)

Rätsch, G.; Demiriz, A.; Bennett, K.: Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces. Machine Learning 48, pp. 193 - 221 (2002)

Müller, K.-R.; Mika, S.; Rätsch, G.; Tsuda, K.; Schölkopf, B.: An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks 12 (2), pp. 181 - 201 (2001)

Schölkopf, B.; Mika, S.; Burges, C.; Knirsch, P.; Müller, K.-R.; Rätsch, G.; Smola, A.: Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 10 (5), pp. 1000 - 1017 (1999)