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Dr. Gunnar Rätsch

Raum Nummer: FML 0.06a
Fax: 07071 601 801

 

Bild von Rätsch, Gunnar, Dr.

Gunnar Rätsch

Position: Wissenschaftler  Abteilung: 

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Tagungsbände (1):

Bousquet O, von Luxburg U und Rätsch G: Advanced Lectures on Machine Learning, ML Summer Schools 2003, 240, Springer, Berlin, Germany, (September-2004).
978-3-540-23122-6, Series: Lecture Notes in Computer Science ; 3176

Artikel (27):

Gan XC, Stegle O, Behr J, Steffen JG, Drewe P, Hildebrand KL, Lyngsoe R, Schultheiss SJ, Osborne EJ, Sreedharan VT, Kahles A, Bohnert R, Jean G, Derwent P, Kersey P, Belfield EJ, Harberd NP, Kemen E, Toomajian C, Kover PX, Clark RM, Rätsch G und Mott R (September-2011) Multiple reference genomes and transcriptomes for Arabidopsis thaliana Nature 477(7365) 419–423.
Stegle O, Drewe P, Bohnert R, Borgwardt K und Rätsch G (Mai-2010) Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts Nature Precedings 2010 1-11.
Schweikert G, Zien A, Zeller G, Behr J, Dieterich C, Ong CS, Philips P, De Bona F, Hartmann L, Bohlen A, Krüger N, Sonnenburg S und Rätsch G (November-2009) mGene: Accurate SVM-based gene finding with an application to nematode genomes Genome Research 19(11) 2133-2143.
Schweikert G, Behr J, Zien A, Zeller G, Ong CS, Sonnenburg S und Rätsch G (Juli-2009) mGene.web: a web service for accurate computational gene finding Nucleic Acids Research 37(Supplement 2) W312-W316.
Graf ABA, Bousquet O, Rätsch G und Schölkopf B (Januar-2009) Prototype Classification: Insights from Machine Learning Neural Computation 21(1) 272-300.
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Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B und Rätsch G (Oktober-2008) Support Vector Machines and Kernels for Computational Biology PLoS Computational Biology 4(10) 1-10.
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Laubinger S, Zeller G, Henz SR, Sachsenberg T, Widmer CK, Naouar N, Vuylsteke M, Schölkopf B, Rätsch G und Weigel D (Juli-2008) At-TAX: A Whole Genome Tiling Array Resource for Developmental Expression Analysis and Transcript Identification in Arabidopsis thaliana Genome Biology 9(7: R112) 1-16.
Sonnenburg S, Zien A, Philips P und Rärsch G (Juli-2008) POIMs: positional oligomer importance matrices - understanding support vector machine-based signal detectors Bioinformatics 24(13) i6-i14.
Sonnenburg S, Schweikert G, Philips P, Behr J und Rätsch G (Dezember-2007) Accurate Splice site Prediction Using Support Vector Machines BMC Bioinformatics 8(Supplement 10) 1-16.
Sonnenburg S, Braun ML, Ong CS, Bengio S, Bottou L, Holmes G, LeCun Y, Müller K-R, Pereira F, Rasmussen CE, Rätsch G, Schölkopf B, Smola A, Vincent P, Weston J und Williamson RC (Oktober-2007) The Need for Open Source Software in Machine Learning Journal of Machine Learning Research 8 2443-2466.
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Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn P, Warthmann N, Hu TT, Fu G, Hinds DA, Chen H, Frazer KA, Huson DH, Schölkopf B, Nordborg M, Rätsch G, Ecker JR und Weigel D (Juli-2007) Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana Science 317(5836) 338-342.
Schulze U, Hepp B, Ong CS und Rätsch G (Mai-2007) PALMA: mRNA to Genome Alignments using Large Margin Algorithms Bioinformatics 23(15) 1892-1900.
Rätsch G, Sonnenburg S, Srinivasan J, Witte H, Müller K-R, Sommer R-J und Schölkopf B (Februar-2007) Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning PLoS Computational Biology 3(2: e20) 0313-0322.
Sonnenburg S, Rätsch G, Schäfer C und Schölkopf B (Juli-2006) Large Scale Multiple Kernel Learning Journal of Machine Learning Research 7 1531-1565.
Tsuda K und Rätsch G (Juni-2005) Image Reconstruction by Linear Programming IEEE Transactions on Image Processing 14(6) 737-744.
Tsuda K, Rätsch G und Warmuth M (Juni-2005) Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection Journal of Machine Learning Research 6 995-1018.
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Mika S, Rätsch G, Weston J, Schölkopf B, Smola AJ und Müller K-R (Mai-2003) Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5) 623-628.
Warmuth MK, Liao J, Rätsch G, Mathieson M, Putta S und Lemmen C (März-2003) Active Learning with SVMs in the Drug Discovery Process Chemical Information and Computer Sciences 43(2) 667ß673-667ß673.
Tsuda K, Kawanabe M, Rätsch G, Sonnenburg S und Müller KR (Oktober-2002) A New Discriminative Kernel from Probabilistic Models Neural Computation 14(10) 2397-2414.
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Rätsch G, Mika S, Schölkopf B und Müller K-R (September-2002) Constructing Boosting algorithms from SVMs: an application to one-class classification IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9) 1184-1199.
Rätsch G, Demiriz A und Bennett K (2002) Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces Machine Learning 48 193-221.
Müller K-R, Mika S, Rätsch G, Tsuda K und Schölkopf B (März-2001) An Introduction to Kernel-Based Learning Algorithms IEEE Transactions on Neural Networks 12(2) 181-201.
Onoda T, Rätsch G und Müller KR (2001) An Arcing algorithm with an intuitive learning control parameter Journal of the Japanese Society for AI 16(5C) 417-426.
Rätsch G, Onoda T und Müller KR (2001) Soft margins for AdaBoost Machine Learning 42 287-320.
Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T und Müller K-R (September-2000) Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites Bioinformatics 16(9) 799-807.
Onoda T, Rätsch G und Müller KR (2000) An asymptotical Analysis and Improvement of AdaBoost in the binary classification case Journal of the Japanese Society for AI 15(2) 287-296.
Schölkopf B, Mika S, Burges CJC, Knirsch P, Müller K-R, Rätsch G und Smola AJ (September-1999) Input space versus feature space in kernel-based methods IEEE Transactions On Neural Networks 10(5) 1000-1017.

Beiträge zu Tagungsbänden (33):

Widmer C, Kloft M, Görnitz N und Rätsch G (September-2012) Efficient Training of Graph-Regularized Multitask SVMs In: Machine Learning and Knowledge Discovery in Databases, , European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2012), Springer, Berlin, Germany, 633-647, Series: Lecture Notes in Computer Science ; 7523.
Widmer C und Rätsch G (2012) Multitask Learning in Computational Biology, Unsupervised and Transfer Learning Workshop, held at ICML 2011, International Machine Learning Society, Madison, WI, USA, 207-216, Series: JMLR Workshop and Conference Proceedings ; 27.
Widmer C, Toussaint NC, Altun Y und Rätsch G (Oktober-2010) Inferring latent task structure for Multitask Learning by Multiple Kernel Learning, NIPS 2009 Workshop on Machine Learning in Computational Biology (MLCB 2009), BioMed Central, London, UK, BMC Bioinformatics, 11(Supplement 8), 1-8.
Widmer C, Toussaint NC, Altun Y, Kohlbacher O und Rätsch G (September-2010) Novel Machine Learning Methods for MHC Class I Binding Prediction In: Pattern Recognition in Bioinformatics, , 5th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2010), Springer, Berlin, Germany, 98-109, Series: Lecture Notes in Computer Science ; 6282.
Widmer C, Leiva J, Altun Y und Rätsch G (April-2010) Leveraging sequence classification by taxonomy-based multitask learning In: Research in Computational Molecular Biology, , 14th International Conference on Research in Computational Molecular Biology (RECOMB 2010), Springer, Berlin, Germany, 522-534, Series: Lecture Notes in Computer Science ; 6044.
Schweikert G, Widmer C, Schölkopf B und Rätsch G (Juni-2009) An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis In: Advances in neural information processing systems 21, , Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 1433-1440.
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Shin H, Hill NJ und Rätsch G (September-2006) Graph Based Semi-Supervised Learning with Sharper Edges In: Machine Learning: ECML 2006, , 17th European Conference on Machine Learning, Springer, Berlin, Germany, 401-412, Series: Lecture Notes in Computer Science ; 4212.
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Rätsch G, Hepp B, Schulze U und Ong CS (September-2006) PALMA: Perfect Alignments using Large Margin Algorithms, German Conference on Bioinformatics (GCB 2006), Gesellschaft für Informatik, Bonn, Germany, 104-113.
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Sonnenburg S, Zien A und Rätsch G (Juli-2006) ARTS: Accurate Recognition of Transcription Starts in Human, 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006), Bioinformatics, 22(14), e472-e480.
Sonnenburg S, Rätsch G und Schölkopf B (August-2005) Large Scale Genomic Sequence SVM Classifiers, 22nd International Conference on Machine Learning (ICML 2005), ACM Press, New York, NY, USA, 848-855.
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Tsuda K, Rätsch G und Warmuth MK (Juli-2005) Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection In: Advances in Neural Information Processing Systems 17, , Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004), MIT Press, Cambridge, MA, USA, 1425-1432.
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Rätsch G, Sonnenburg S und Schölkopf B (Juni-2005) RASE: recognition of alternatively spliced exons in C.elegans, Thirteenth International Conference on Intelligent Systems for Molecular Biology (ISBM 2005), Bioinformatics, 21(Supplement 1), i369-i377.
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Tsuda K und Rätsch G (Juni-2004) Image Construction by Linear Programming In: Advances in Neural Information Processing Systems 16, , Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), MIT Press, Cambridge, MA, USA, 57-64.
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Rätsch G, Smola A und Mika S (Oktober-2003) Adapting Codes and Embeddings for Polychotomies In: Advances in Neural Information Processing Systems 15, , Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002), MIT Press, Cambridge, MA, USA, 513-520.
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Meir R und Rätsch G (2003) An Introduction to Boosting and Leveraging In: Advanced Lectures on Machine Learning, , Machine Learning Summer School 2002, Springer, Berlin, Germany, 119-184, Series: Lecture Notes in Computer Science ; 2600.
Tsuda K, Kawanabe M, Rätsch G, Sonnenburg S und Müller K-R (September-2002) A new discriminative kernel from probabilistic models In: Advances in Neural Information Processing Systems 14, , Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001), MIT Press, Cambridge, MA, USA, 977-984.
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Warmuth MK, Rätsch G, Mathieson M, Liao J und Lemmen C (September-2002) Active Learning in the Drug Discovery Process In: Advances in Neural Information Processing Systems 14, , Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001), MIT Press, Cambridge, MA, USA, 1449-1456.
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Rätsch G, Mika S und Warmuth MK (September-2002) On the Convergence of Leveraging In: Advances in Neural Information Processing Systems 14, , Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001), MIT Press, Cambridge, MA, USA, 487-494.
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Sonnenburg S, Rätsch G, Jagota A und Müller K-R (August-2002) New methods for Splice Site recognition In: Artificial Neural Networks - ICANN 2002, , International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 329-336, Series: Lecture Notes in Computer Science ; 2415.
Rätsch G und Warmuth MK (Juli-2002) Maximizing the Margin with Boosting In: Computational Learning Theory, , 15th Annual Conference on Computational Learning Theory (COLT 2002), Springer, Berlin, Germany, 334-350, Series: Lecture Notes in Computer Science ; 2375.
Tsuda K, Rätsch G, Mika S und Müller K-R (August-2001) Learning to predict the leave-one-out error of kernel based classifiers In: Artificial Neural Networks - ICANN 2001, , International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 331-338, Series: Lecture Notes in Computer Science ; 2130.
Rätsch G, Warmuth MK, Mika S, Onoda T, Lemm S und Müller K-R (Juli-2000) Barrier Boosting, 13th Annual Conference on Computational Learning Theory (COLT 2000), Morgan Kaufmann, San Francisco, CA, USA, 170-179.
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Last updated: Montag, 22.05.2017