Search results

Journal Article (21)

1.
Journal Article
Gan, X.; Stegle, O.; Behr, J.; Steffen, J.; Drewe, P.; Hildebrand, K.; Lyngsoe , R.; Schultheiss, S.; Osborne, E.; Sreedharan, V. et al.; Kahles, A.; Bohnert, R.; Jean, G.; Derwent, P.; Kersey, P.; Belfield, E.; Harberd, N.; Kemen, F.; Toomajian, C.; Kover , P.; Clark, R.; Rätsch, G.; Mott, R.: Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature 477 (7365), pp. 419 - 423 (2011)
2.
Journal Article
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)
3.
Journal Article
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)
4.
Journal Article
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)
5.
Journal Article
Graf, A.; Bousquet, O.; Rätsch, G.; Schölkopf, B.: Prototype Classification: Insights from Machine Learning. Neural computation 21 (1), pp. 272 - 300 (2009)
6.
Journal Article
Ben-Hur, A.; Ong, C.; Sonnenburg, S.; Schölkopf, B.; Rätsch, G.: Support Vector Machines and Kernels for Computational Biology. PLoS Computational Biology 4 (10), pp. 1 - 10 (2008)
7.
Journal Article
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)
8.
Journal Article
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)
9.
Journal Article
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)
10.
Journal Article
Schulze, U.; Hepp, B.; Ong, C.; Rätsch, G.: PALMA: mRNA to Genome Alignments using Large Margin Algorithms. Bioinformatics 23 (15), pp. 1892 - 1900 (2007)
11.
Journal Article
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)
12.
Journal Article
Sonnenburg, S.; Rätsch, G.; Schäfer, C.; Schölkopf, B.: Large Scale Multiple Kernel Learning. The Journal of Machine Learning Research 7, pp. 1531 - 1565 (2006)
13.
Journal Article
Tsuda, K.; Rätsch, G.: Image Reconstruction by Linear Programming. IEEE Transactions on Image Processing 14 (6), pp. 737 - 744 (2005)
14.
Journal Article
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)
15.
Journal Article
Mika, S.; Rätsch, G.; Weston, J.; Schölkopf, B.; Smola, A.; Müller, K.-R.: Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (5), pp. 623 - 628 (2003)
16.
Journal Article
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)
17.
Journal Article
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)
18.
Journal Article
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)
19.
Journal Article
Rätsch, G.; Demiriz, A.; Bennett, K.: Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces. Machine Learning 48, pp. 193 - 221 (2002)
20.
Journal Article
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)
Go to Editor View