Gunnar Rätsch

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

Coauthor Index

1	Kristin P. Bennett	[19]
2	Fabio De Bona	[51]
3	Olivier Bousquet	[29]
4	Christopher J. C. Burges (Chris Burges)	[5]
5	Wolfgang Busch	[50]
6	Ayhan Demiriz	[19]
7	Karen A. Glocer	[46]
8	Michael Grimm	[32]
9	Nikolaus Heinrich	[32]
10	Stefan R. Henz	[47]
11	Bettina Hepp	[45]
12	N. Jeremy Hill	[42]
13	Arun K. Jagota	[23]
14	Motoaki Kawanabe	[14] [18]
15	Phil Knirsch	[5]
16	Oliver Kohlbacher	[50]
17	Jens Kohlmorgen	[1]
18	Sascha Laubinger	[47]
19	Steven Lemm	[12]
20	Christian Lemmen	[8] [16] [25]
21	Thomas Lengauer	[8] [9]
22	Jun Liao	[16] [25] [41]
23	Jan Lohmann	[50]
24	Ulrike von Luxburg	[29]
25	Michael Mathieson	[16] [25]
26	Ron Meir (Ronny Meir)	[22]
27	Sebastian Mika	[3] [5] [6] [7] [8] [9] [10] [11] [12] [15] [17] [20] [21] [26] [32]
28	Klaus-Robert Müller	[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [17] [18] [20] [23] [26] [32]
29	Cheng Soon Ong	[45]
30	Takashi Onoda	[2] [4] [6] [10] [12] [13]
31	Stephan Ossowski	[51]
32	Petra Philips	[49]
33	Santosh Putta	[25]
34	Christin Schäfer	[34] [35] [38]
35	Korbinian Schneeberger	[51]
36	Bernhard Schölkopf	[1] [3] [5] [6] [7] [8] [9] [10] [20] [26] [36] [37] [38] [48]
37	Matthias Scholz	[3]
38	Sebastian J. Schultheiß	[50]
39	Uta Schulze	[45]
40	Gabriele Schweikert	[48]
41	Hyunjung Shin (Hyunjung (Helen) Shin)	[42]
42	Alexander J. Smola (Alex J. Smola)	[1] [3] [5] [6] [7] [8] [10] [21] [26]
43	Sören Sonnenburg	[14] [18] [23] [32] [34] [35] [36] [37] [38] [39] [40] [49]
44	Koji Tsuda	[14] [17] [18] [27] [28] [30] [33]
45	Vladimir Vapnik	[1]
46	Manfred K. Warmuth	[12] [15] [16] [24] [25] [28] [30] [31] [41] [46]
47	Detlef Weigel	[47]
48	Jason Weston	[7] [26]
49	Christian Widmer	[48]
50	Georg Zeller	[47]
51	Alexander Zien	[8] [9] [40] [49]

Colors in the list of coauthors