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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2007 Schulze, U, Hepp, B, Ong, CS, and Rätsch, G (2007).
PALMA: mRNA to Genome Alignments using Large Margin Algorithms
Bioinformatics, 23(15):1892-1900.
Online Access Printable file

Schweikert, G, Sonnenburg, S, Philips, P, Behr, J, and Rätsch, G (2007).
Accurate splice site prediction using support vector machines
BMC Bioinformatics, 8(Suppl. 10):S7.
Online Access Supplementary website Printable file

Shin, H and Cho, S (2007).
Neighborhood Property based Pattern Selection for Support Vector Machines
Neural Computation, 19(3):816-855.

Sonnenburg, S, Braun, ML, Ong, CS, Bengio, S, Bottou, L, Holmes, G, LeCun, Y, Müller, K, Pereira, F, Rasmussen, CE, Rätsch, G, Schölkopf, B, Smola, A, Vincent, P, Weston, J, and Williamson, RC (2007).
The Need for Open Source Software in Machine Learning
Journal of Machine Learning Research, 8:2443-2466.
Online Access Printable file

Sonnenburg, S, Rätsch, G, and Rieck, K (2007).
Large Scale Learning with String Kernels
In: Large-Scale Kernel Machines, ed. by Léon Bottou, Olivier Chapelle, Dennis DeCoste and Jason Weston. MIT Press, Cambridge, MA, chap. 4, pp. 73-104.
Printable file

Zien, A and Ong, CS (2007).
Multiclass Multiple Kernel Learning
In: Proceedings of the 24th International Conference on Machine Learning (ICML), pp. 1191-1198, New York, NY, USA, ACM Press.
Online Access

Zien, A, Brefeld, U, and Scheffer, T (2007).
Transductive Support Vector Machines for Structured Variables
In: Proceedings of the 24th International Conference on Machine Learning (ICML), pp. 1183-1190, New York, NY, USA, ACM Press.
Online Access Printable file

Zien, A, De Bona, F, and Ong, CS (2007).
Training and Approximation of a Primal Multiclass Support Vector Machine
In: 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA).
Online Access Printable file

Zien, A, Gebhard, PM, Fundel, K, and Aigner, T (2007).
Phenotyping of Chondrocytic Cells Using the cDNA Array Technology
Clinical Orthopaedics and Related Research, 460:226-233.

Zien, A, Sonnenburg, S, and Philips, P (2007).
Computing Positional Oligomer Importance Matrices (POIMs)
Fraunhofer Institute FIRST, technical report(2).
Online Access Printable file

2006 Aigner, T, Fundel, K, Saas, J, Gebhard, PM, Haag, J, Weiss, T, Zien, A, Obermayr, F, Zimmer, R, and Bartnik, E (2006).
Large-Scale Gene Expression Profiling Reveals Major Pathogenetic Pathways of Cartilage Degeneration in Osteoarthritis
Arthritis and Rheumatism, 54(11):3533-3544.
Online Access

Chapelle, O, Chi, M, and Zien, A (2006).
A Continuation Method for Semi-Supervised SVMs
In: Proceedings of the 23rd International Conference on Machine Learning (ICML), pp. 185-192, New York, NY, USA, ACM Press.
Online Access Printable file

Chapelle, O, Schölkopf, B, and Zien, A (2006).
Semi-Supervised Learning
The MIT Press, Cambridge, MA, USA. (ISBN: 978-0-262-03358-9).
Online Access

Chechik, G, Leslie, C, Rätsch, G, and Tsuda, K (2006).
New Problems and Methods in Computational Biology
BMC Bioinformatics, vol. 7(Suppl. 1).
Online Access

Cho, S, Shin, H, Yu, E, Ha, K, and MacLachlan, D (2006).
Data mining problems and solutions for response modeling in CRM
Entrue Journal of Information Technology, 5(1):55-64.

Rätsch, G, Hepp, B, Schulze, U, and Ong, CS (2006).
PALMA: Perfect Alignments using Large Margin Algorithms
In: German Conference on Bioinformatics, pp. 104-113, Berlin,Heidelberg, Springer Verlag. LNCS.
Online Access Printable file

Shin, H and Tsuda, K (2006).
Chapter 20: Prediction of Protein Function from Networks
In: Semi-Supervised Learning. MIT press, chap. Chapter 20: Prediction of Protein Function from Networks, pp. 339-352.

Shin, H, Hill, NJ, and Raetsch, G (2006).
Graph-based Semi-Supervised Learning with Sharper Edges
Lecture Notes in Artificial Intelligence , 4212:402-413.

Sonnenburg, S, Rätsch, G, and Schäfer, C (2006).
A general and efficient multiple kernel learning algorithm
In: Advances in Neural Information Processing Systems (NIPS'08), ed. by Y. Weiss and B. Schölkopf and J. Platt, vol. 15, pp. 1273-1280, Cambridge, MA, MIT Press.
Online Access

Sonnenburg, S, Rätsch, G, Schäfer, C, and Schölkopf, B (2006).
Large scale multiple kernel learning
Journal of Machine Learning Research, 7:1531-1565.
Printable file

Sonnenburg, S, Zien, A, and Rätsch, G (2006).
ARTS: Accurate Recognition of Transcription Starts in Human
Bioinformatics, 22(14):e472-e480.
Online Access

Warmuth, MK, Liao, J, and Rätsch, G (2006).
Totally Corrective Boosting Algorithms that Maximize the Margin
In: Proceedings of the International Conference on Machine Learning, ed. by William Cohen and Andrew Moore, pp. 1001-1008, Pittsburg. IMLS.
Online Access

Wolf, T, Brors, B, Hofmann, T, and Georgii, E (2006).
Global Biclustering of Microarray Data
In: Sixth IEEE International Conference on Data Mining, pp. 125-129, Los Alamitos, CA, USA, IEEE Computer Society.
Printable file

Zien, A, Ong, CS, and Rätsch, G (2006).
Towards the Inference of Graphs on Ordered Vertices
Max Planck Institute for biological Cybernetics, Research Note(150), Tübingen, Germany.
Online Access Printable file

2005 Borgwardt, KM, Ong, CS, Sch\"onauer, S, Vishwanathan, S, Smola, AJ, and Kriegel, H (2005).
Protein Function Prediction via Graph Kernels
In: Proceedings of the International Conference on Intelligent Systems for Molecular Biology.

 
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