ARCHIVE Snapshot of the Rätsch lab website (Tübingen, then New York) as of February 2016: preserved, not maintained. The group today: bmi.inf.ethz.ch
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Bibliography

References to publications, sorted by year and author.
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2005 Canu, S, Ong, CS, and Mary, X (2005).
Splines with Non Positive Kernels
In: 5th International ISAAC Congress.

Georgii, E, Richter, L, Rückert, U, and Kramer, S (2005).
Analyzing microarray data using quantitative association rules
Bioinformatics, 21(Suppl. 2):123-129.
Printable file

Müller, K, Rätsch, G, Sonnenburg, S, Mika, S, Grimm, M, and Heinrich, N (2005).
Classifying 'Drug-likeness' with Kernel-Based Learning Methods
J. Chem. Inf. Model, 45:249-253.
Online Access

Ong, CS, Smola, AJ, and Williamson, RC (2005).
Learning the Kernel with Hyperkernels
Journal of Machine Learning Research, 6:1043–1071.

Philips, P (2005).
Data-Dependent Analysis of Learning Algorithms
PhD Thesis, The Australian National University, Canberra, Australia.

Rätsch, G and Warmuth, M (2005).
Efficient Margin Maximization with Boosting
Journal of Machine Learning Research, 6:2131-2152.
Online Access

Rätsch, G, Sonnenburg, S, and Schölkopf, B (2005).
RASE: Recognition of alternatively Spliced Exons in C. elegans
In: Bioinformatics, vol. 21(Suppl. 1), pp. i369.
Online Access

Shin, H (2005).
Efficient Pattern Selection for Support Vector Classifiers and its CRM Application
PhD thesis, Seoul National University.

Shin, H and Cho, S (2005).
Response Modeling with Support Vector Machines
Expert Systems with Applications, 30(4):746-760.

Shin, H and Cho, S (2005).
Invariance of Neighborhood Relation under Input Space to Feature Space Mapping
Pattern Recognition Letters, 26(6):707-718.

Sonnenburg, S, Rätsch, G, and Schäfer, C (2005).
Learning interpretable SVMs for biological sequence classification
In: RECOMB 2005, LNBI 3500, pp. 389-407, Berlin Heidelberg, Springer-Verlag.
Printable file

Sonnenburg, S, Rätsch, G, and Schölkopf, B (2005).
Large Scale Genomic Sequence SVM Classifiers
In: Proceedings of the International Conference on Machine Learning, ICML.
Online Access

Tsuda, K and Rätsch, G (2005).
Image reconstruction by linear programming
IEEE Transactions on Image Processing, 14(6):737-744.
Online Access Printable file

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

Tsuda, K, Shin, H, and Schoelkopf, B (2005).
Fast Protein Classification with Multiple Networks
Bioinformatics, 21(2):59-65.

Zeller, G (2005).
Resequencing Data of 20 Arabidopsis Ecotypes
Diplomarbeit, Universität Tübingen.
Printable file

2004 Bartlett, PL, Mendelson, S, and Philips, P (2004).
Local Complexities for Empirical Risk Minimization
In: Proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, ed. by J. Shawe-Taylor and Y. Singer, pp. 270-284, Springer. LNCS 3120.

Knabe, S, Mika, S, Müller, K, Rätsch, G, and Schruff, W (2004).
Zur Beurteilung des Fraud-Risikos im Rahmen der Abschlussprüfung
Die Wirtschaftsprüfung, 19(04):1058-1068.

Mendelson, S and Philips, P (2004).
On the Importance of Small Coordinate Projections
Journal of Machine Learning Research, 5.
Online Access Printable file

Ong, CS, Mary, X, Canu, S, and Smola, AJ (2004).
Learning with Non-Positive Kernels
In: International Conference on Machine Learning, pp. 639–646.

Rätsch, G and Sonnenburg, S (2004).
Accurate Splice Site Detection for Caenorhabditis elegans
In: Kernel Methods in Computational Biology, ed. by B. Schölkopf, K. Tsuda and J.-P. Vert, pp. 277-298, MIT Press.
Online Access

Shin, H and Cho, S (2004).
Preservation of Neighborhood Relation under Input to Feature Space Mapping in SVM Training
Proc. of the 33rd International Conference on Computers and Industrial Engineering (C&IE), in CD.

Shin, H, Tsuda, K, Cho, S, and Bernhard Schoelkopf (2004).
Protein Functional Class Prediction with a Combined Graph: EM-like Optimization
Proc. of Korean Data Mining Society Conference:135-155.

Sun, J, Cho, S, and Shin, H (2004).
Pattern Selection for SVM based Futures Trading System
Proc. of the Korean Data Mining Society Conference:175-183.

2003 Jeong, D, Kim, Y, Cho, S, and Shin, H (2003).
A Study on Rainfall-Runoff Models for Improving Ensemble Streamflow Prediction: 1. Rainfall-runoff Models Using Artificial Neural Networks
Journal of the Korean Society of Civil Engineers, 23(6B):521-530.

 
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