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Gunnar Rätsch: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Gunnar Rätsch
    Solving Semi-infinite Linear Programs Using Boosting-Like Methods. [Citation Graph (0, 0)][DBLP]
    ALT, 2006, pp:10-11 [Conf]
  2. Gunnar Rätsch, Manfred K. Warmuth
    Maximizing the Margin with Boosting. [Citation Graph (0, 0)][DBLP]
    COLT, 2002, pp:334-350 [Conf]
  3. Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller
    Barrier Boosting. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:170-179 [Conf]
  4. Gunnar Rätsch
    The Solution of Semi-Infinite Linear Programs Using Boosting-Like Methods. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2006, pp:15- [Conf]
  5. HyunJung Shin, N. Jeremy Hill, Gunnar Rätsch
    Graph Based Semi-supervised Learning with Sharper Edges. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:401-412 [Conf]
  6. 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. [Citation Graph (0, 0)][DBLP]
    German Conference on Bioinformatics, 1999, pp:37-43 [Conf]
  7. Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik
    Predicting Time Series with Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:999-1004 [Conf]
  8. Sören Sonnenburg, Gunnar Rätsch, Arun K. Jagota, Klaus-Robert Müller
    New Methods for Splice Site Recognition. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:329-336 [Conf]
  9. Koji Tsuda, Gunnar Rätsch, Sebastian Mika, Klaus-Robert Müller
    Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:331-338 [Conf]
  10. Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf
    Large scale genomic sequence SVM classifiers. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:848-855 [Conf]
  11. Manfred K. Warmuth, Jun Liao, Gunnar Rätsch
    Totally corrective boosting algorithms that maximize the margin. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:1001-1008 [Conf]
  12. Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller
    An Improvement of AdaBoost to Avoid Overfitting. [Citation Graph (0, 0)][DBLP]
    ICONIP, 1998, pp:506-509 [Conf]
  13. Gunnar Rätsch, Sören Sonnenburg, Bernhard Schölkopf
    RASE: recognition of alternatively spliced exons in C.elegans. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2005, pp:369-377 [Conf]
  14. Sören Sonnenburg, Alexander Zien, Gunnar Rätsch
    ARTS: accurate recognition of transcription starts in human. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2006, pp:472-480 [Conf]
  15. Ron Meir, Gunnar Rätsch
    An Introduction to Boosting and Leveraging. [Citation Graph (0, 0)][DBLP]
    Machine Learning Summer School, 2002, pp:118-183 [Conf]
  16. Sebastian Mika, Gunnar Rätsch, Klaus-Robert Müller
    A Mathematical Programming Approach to the Kernel Fisher Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:591-597 [Conf]
  17. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller
    Invariant Feature Extraction and Classification in Kernel Spaces. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:526-532 [Conf]
  18. Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch
    Kernel PCA and De-Noising in Feature Spaces. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:536-542 [Conf]
  19. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika
    v-Arc: Ensemble Learning in the Presence of Outliers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:561-567 [Conf]
  20. Gunnar Rätsch, Alexander J. Smola, Sebastian Mika
    Adapting Codes and Embeddings for Polychotomies. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:513-520 [Conf]
  21. Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth
    On the Convergence of Leveraging. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:487-494 [Conf]
  22. Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller
    Regularizing AdaBoost. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:564-570 [Conf]
  23. Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer
    A General and Efficient Multiple Kernel Learning Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  24. Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller
    A New Discriminative Kernel From Probabilistic Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:977-984 [Conf]
  25. Koji Tsuda, Gunnar Rätsch
    Image Reconstruction by Linear Programming. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  26. Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth
    Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  27. Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen
    Active Learning in the Drug Discovery Process. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1449-1456 [Conf]
  28. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller
    Robust Ensemble Learning for Data Mining. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:341-344 [Conf]
  29. Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer
    Learning Interpretable SVMs for Biological Sequence Classification. [Citation Graph (0, 0)][DBLP]
    RECOMB, 2005, pp:389-407 [Conf]
  30. Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Thomas Lengauer, Klaus-Robert Müller
    Engineering support vector machine kernels that recognize translation initiation sites. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2000, v:16, n:9, pp:799-807 [Journal]
  31. Klaus-Robert Müller, Gunnar Rätsch, Sören Sonnenburg, Sebastian Mika, Michael Grimm, Nikolaus Heinrich
    Classifying 'Drug-likeness' with Kernel-Based Learning Methods. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Modeling, 2005, v:45, n:2, pp:249-253 [Journal]
  32. Manfred K. Warmuth, Jun Liao, Gunnar Rätsch, Michael Mathieson, Santosh Putta, Christian Lemmen
    Active Learning with Support Vector Machines in the Drug Discovery Process. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 2003, v:43, n:2, pp:667-673 [Journal]
  33. Gunnar Rätsch, Manfred K. Warmuth
    Efficient Margin Maximizing with Boosting. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:2131-2152 [Journal]
  34. Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth
    Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:995-1018 [Journal]
  35. Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf
    Large Scale Multiple Kernel Learning. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:1531-1565 [Journal]
  36. Gunnar Rätsch, Ayhan Demiriz, Kristin P. Bennett
    Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:48, n:1-3, pp:189-218 [Journal]
  37. Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller
    Soft Margins for AdaBoost. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:42, n:3, pp:287-320 [Journal]
  38. Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller
    A New Discriminative Kernel from Probabilistic Models. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2002, v:14, n:10, pp:2397-2414 [Journal]
  39. 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. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2003, v:25, n:5, pp:623-633 [Journal]
  40. Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Klaus-Robert Müller
    Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2002, v:24, n:9, pp:1184-1199 [Journal]
  41. Koji Tsuda, Gunnar Rätsch
    Image reconstruction by linear programming. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Image Processing, 2005, v:14, n:6, pp:737-744 [Journal]
  42. Gunnar Rätsch, Sören Sonnenburg
    Large Scale Hidden Semi-Markov SVMs. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1161-1168 [Conf]

  43. Optimal spliced alignments of short sequence reads. [Citation Graph (, )][DBLP]


  44. KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences. [Citation Graph (, )][DBLP]


  45. PALMA: Perfect Alignments using Large Margin Algorithms. [Citation Graph (, )][DBLP]


  46. POIMs: positional oligomer importance matrices - understanding support vector machine-based signal detectors. [Citation Graph (, )][DBLP]


  47. Boosting Algorithms for Maximizing the Soft Margin. [Citation Graph (, )][DBLP]


  48. An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. [Citation Graph (, )][DBLP]


  49. The Feature Importance Ranking Measure. [Citation Graph (, )][DBLP]


  50. Transcript Normalization and Segmentation of Tiling Array Data. [Citation Graph (, )][DBLP]


  51. Leveraging Sequence Classification by Taxonomy-Based Multitask Learning. [Citation Graph (, )][DBLP]


  52. Novel Machine Learning Methods for MHC Class I Binding Prediction. [Citation Graph (, )][DBLP]


  53. PALMA: mRNA to genome alignments using large margin algorithms. [Citation Graph (, )][DBLP]


  54. KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. [Citation Graph (, )][DBLP]


  55. Accurate splice site prediction using support vector machines. [Citation Graph (, )][DBLP]


  56. NIPS workshop on New Problems and Methods in Computational Biology. [Citation Graph (, )][DBLP]


  57. Learning Interpretable SVMs for Biological Sequence Classification. [Citation Graph (, )][DBLP]


  58. Optimal spliced alignments of short sequence reads. [Citation Graph (, )][DBLP]


  59. Revealing sequence variation patterns in rice with machine learning methods. [Citation Graph (, )][DBLP]


  60. KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. [Citation Graph (, )][DBLP]


  61. Transcript quantification with RNA-Seq data. [Citation Graph (, )][DBLP]


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