2008 |
51 | EE | Fabio De Bona,
Stephan Ossowski,
Korbinian Schneeberger,
Gunnar Rätsch:
Optimal spliced alignments of short sequence reads.
ECCB 2008: 174-180 |
50 | | Sebastian J. Schultheiß,
Wolfgang Busch,
Jan Lohmann,
Oliver Kohlbacher,
Gunnar Rätsch:
KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences.
German Conference on Bioinformatics 2008: 158-167 |
49 | EE | Sören Sonnenburg,
Alexander Zien,
Petra Philips,
Gunnar Rätsch:
POIMs: positional oligomer importance matrices - understanding support vector machine-based signal detectors.
ISMB 2008: 6-14 |
48 | EE | Gabriele Schweikert,
Christian Widmer,
Bernhard Schölkopf,
Gunnar Rätsch:
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis.
NIPS 2008: 1433-1440 |
47 | EE | Georg Zeller,
Stefan R. Henz,
Sascha Laubinger,
Detlef Weigel,
Gunnar Rätsch:
Transcript Normalization and Segmentation of Tiling Array Data.
Pacific Symposium on Biocomputing 2008: 527-538 |
2007 |
46 | EE | Manfred K. Warmuth,
Karen A. Glocer,
Gunnar Rätsch:
Boosting Algorithms for Maximizing the Soft Margin.
NIPS 2007 |
45 | EE | Uta Schulze,
Bettina Hepp,
Cheng Soon Ong,
Gunnar Rätsch:
PALMA: mRNA to genome alignments using large margin algorithms.
Bioinformatics 23(15): 1892-1900 (2007) |
2006 |
44 | EE | Gunnar Rätsch:
Solving Semi-infinite Linear Programs Using Boosting-Like Methods.
ALT 2006: 10-11 |
43 | EE | Gunnar Rätsch:
The Solution of Semi-Infinite Linear Programs Using Boosting-Like Methods.
Discovery Science 2006: 15 |
42 | EE | Hyunjung Shin,
N. Jeremy Hill,
Gunnar Rätsch:
Graph Based Semi-supervised Learning with Sharper Edges.
ECML 2006: 401-412 |
41 | EE | Manfred K. Warmuth,
Jun Liao,
Gunnar Rätsch:
Totally corrective boosting algorithms that maximize the margin.
ICML 2006: 1001-1008 |
40 | EE | Sören Sonnenburg,
Alexander Zien,
Gunnar Rätsch:
ARTS: accurate recognition of transcription starts in human.
ISMB (Supplement of Bioinformatics) 2006: 472-480 |
39 | EE | Gunnar Rätsch,
Sören Sonnenburg:
Large Scale Hidden Semi-Markov SVMs.
NIPS 2006: 1161-1168 |
38 | EE | Sören Sonnenburg,
Gunnar Rätsch,
Christin Schäfer,
Bernhard Schölkopf:
Large Scale Multiple Kernel Learning.
Journal of Machine Learning Research 7: 1531-1565 (2006) |
2005 |
37 | EE | Sören Sonnenburg,
Gunnar Rätsch,
Bernhard Schölkopf:
Large scale genomic sequence SVM classifiers.
ICML 2005: 848-855 |
36 | EE | Gunnar Rätsch,
Sören Sonnenburg,
Bernhard Schölkopf:
RASE: recognition of alternatively spliced exons in C.elegans.
ISMB (Supplement of Bioinformatics) 2005: 369-377 |
35 | EE | Sören Sonnenburg,
Gunnar Rätsch,
Christin Schäfer:
A General and Efficient Multiple Kernel Learning Algorithm.
NIPS 2005 |
34 | EE | Sören Sonnenburg,
Gunnar Rätsch,
Christin Schäfer:
Learning Interpretable SVMs for Biological Sequence Classification.
RECOMB 2005: 389-407 |
33 | EE | Koji Tsuda,
Gunnar Rätsch:
Image reconstruction by linear programming.
IEEE Transactions on Image Processing 14(6): 737-744 (2005) |
32 | EE | Klaus-Robert Müller,
Gunnar Rätsch,
Sören Sonnenburg,
Sebastian Mika,
Michael Grimm,
Nikolaus Heinrich:
Classifying 'Drug-likeness' with Kernel-Based Learning Methods.
Journal of Chemical Information and Modeling 45(2): 249-253 (2005) |
31 | EE | Gunnar Rätsch,
Manfred K. Warmuth:
Efficient Margin Maximizing with Boosting.
Journal of Machine Learning Research 6: 2131-2152 (2005) |
30 | EE | Koji Tsuda,
Gunnar Rätsch,
Manfred K. Warmuth:
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection.
Journal of Machine Learning Research 6: 995-1018 (2005) |
2004 |
29 | | Olivier Bousquet,
Ulrike von Luxburg,
Gunnar Rätsch:
Advanced Lectures on Machine Learning, ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures
Springer 2004 |
28 | EE | Koji Tsuda,
Gunnar Rätsch,
Manfred K. Warmuth:
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection.
NIPS 2004 |
2003 |
27 | EE | Koji Tsuda,
Gunnar Rätsch:
Image Reconstruction by Linear Programming.
NIPS 2003 |
26 | EE | Sebastian Mika,
Gunnar Rätsch,
Jason Weston,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller:
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces.
IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003) |
25 | EE | Manfred K. Warmuth,
Jun Liao,
Gunnar Rätsch,
Michael Mathieson,
Santosh Putta,
Christian Lemmen:
Active Learning with Support Vector Machines in the Drug Discovery Process.
Journal of Chemical Information and Computer Sciences 43(2): 667-673 (2003) |
2002 |
24 | EE | Gunnar Rätsch,
Manfred K. Warmuth:
Maximizing the Margin with Boosting.
COLT 2002: 334-350 |
23 | EE | Sören Sonnenburg,
Gunnar Rätsch,
Arun K. Jagota,
Klaus-Robert Müller:
New Methods for Splice Site Recognition.
ICANN 2002: 329-336 |
22 | EE | Ron Meir,
Gunnar Rätsch:
An Introduction to Boosting and Leveraging.
Machine Learning Summer School 2002: 118-183 |
21 | EE | Gunnar Rätsch,
Alexander J. Smola,
Sebastian Mika:
Adapting Codes and Embeddings for Polychotomies.
NIPS 2002: 513-520 |
20 | EE | Gunnar Rätsch,
Sebastian Mika,
Bernhard Schölkopf,
Klaus-Robert Müller:
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification.
IEEE Trans. Pattern Anal. Mach. Intell. 24(9): 1184-1199 (2002) |
19 | | Gunnar Rätsch,
Ayhan Demiriz,
Kristin P. Bennett:
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces.
Machine Learning 48(1-3): 189-218 (2002) |
18 | EE | Koji Tsuda,
Motoaki Kawanabe,
Gunnar Rätsch,
Sören Sonnenburg,
Klaus-Robert Müller:
A New Discriminative Kernel from Probabilistic Models.
Neural Computation 14(10): 2397-2414 (2002) |
2001 |
17 | EE | Koji Tsuda,
Gunnar Rätsch,
Sebastian Mika,
Klaus-Robert Müller:
Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers.
ICANN 2001: 331-338 |
16 | EE | Manfred K. Warmuth,
Gunnar Rätsch,
Michael Mathieson,
Jun Liao,
Christian Lemmen:
Active Learning in the Drug Discovery Process.
NIPS 2001: 1449-1456 |
15 | EE | Gunnar Rätsch,
Sebastian Mika,
Manfred K. Warmuth:
On the Convergence of Leveraging.
NIPS 2001: 487-494 |
14 | EE | Koji Tsuda,
Motoaki Kawanabe,
Gunnar Rätsch,
Sören Sonnenburg,
Klaus-Robert Müller:
A New Discriminative Kernel From Probabilistic Models.
NIPS 2001: 977-984 |
13 | | Gunnar Rätsch,
Takashi Onoda,
Klaus-Robert Müller:
Soft Margins for AdaBoost.
Machine Learning 42(3): 287-320 (2001) |
2000 |
12 | | Gunnar Rätsch,
Manfred K. Warmuth,
Sebastian Mika,
Takashi Onoda,
Steven Lemm,
Klaus-Robert Müller:
Barrier Boosting.
COLT 2000: 170-179 |
11 | | Sebastian Mika,
Gunnar Rätsch,
Klaus-Robert Müller:
A Mathematical Programming Approach to the Kernel Fisher Algorithm.
NIPS 2000: 591-597 |
10 | | Gunnar Rätsch,
Bernhard Schölkopf,
Alex J. Smola,
Sebastian Mika,
Takashi Onoda,
Klaus-Robert Müller:
Robust Ensemble Learning for Data Mining.
PAKDD 2000: 341-344 |
9 | | Alexander Zien,
Gunnar Rätsch,
Sebastian Mika,
Bernhard Schölkopf,
Thomas Lengauer,
Klaus-Robert Müller:
Engineering support vector machine kernels that recognize translation initiation sites.
Bioinformatics 16(9): 799-807 (2000) |
1999 |
8 | | Alexander Zien,
Gunnar Rätsch,
Sebastian Mika,
Bernhard Schölkopf,
Christian Lemmen,
Alex J. Smola,
Thomas Lengauer,
Klaus-Robert Müller:
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites.
German Conference on Bioinformatics 1999: 37-43 |
7 | EE | Sebastian Mika,
Gunnar Rätsch,
Jason Weston,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller:
Invariant Feature Extraction and Classification in Kernel Spaces.
NIPS 1999: 526-532 |
6 | EE | Gunnar Rätsch,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller,
Takashi Onoda,
Sebastian Mika:
v-Arc: Ensemble Learning in the Presence of Outliers.
NIPS 1999: 561-567 |
5 | EE | Bernhard Schölkopf,
Sebastian Mika,
Christopher J. C. Burges,
Phil Knirsch,
Klaus-Robert Müller,
Gunnar Rätsch,
Alexander J. Smola:
Input space versus feature space in kernel-based methods.
IEEE Transactions on Neural Networks 10(5): 1000-1017 (1999) |
1998 |
4 | | Gunnar Rätsch,
Takashi Onoda,
Klaus-Robert Müller:
An Improvement of AdaBoost to Avoid Overfitting.
ICONIP 1998: 506-509 |
3 | EE | Sebastian Mika,
Bernhard Schölkopf,
Alex J. Smola,
Klaus-Robert Müller,
Matthias Scholz,
Gunnar Rätsch:
Kernel PCA and De-Noising in Feature Spaces.
NIPS 1998: 536-542 |
2 | EE | Gunnar Rätsch,
Takashi Onoda,
Klaus-Robert Müller:
Regularizing AdaBoost.
NIPS 1998: 564-570 |
1997 |
1 | | Klaus-Robert Müller,
Alex J. Smola,
Gunnar Rätsch,
Bernhard Schölkopf,
Jens Kohlmorgen,
Vladimir Vapnik:
Predicting Time Series with Support Vector Machines.
ICANN 1997: 999-1004 |