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Sebastian Mika
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2010 – 2019
- 2013
- [c14]John C. Snyder, Sebastian Mika, Kieron Burke, Klaus-Robert Müller:
Kernels, Pre-images and Optimization. Empirical Inference 2013: 245-259
2000 – 2009
- 2009
- [j13]Katja Hansen, Fabian Rathke, Timon Schroeter, Georg Rast, Thomas Fox
, Jan M. Kriegl, Sebastian Mika:
Bias-Correction of Regression Models: A Case Study on hERG Inhibition. J. Chem. Inf. Model. 49(6): 1486-1496 (2009) - [j12]Katja Hansen, Sebastian Mika, Timon Schroeter, Andreas Sutter, Antonius ter Laak
Benchmark Data Set for in Silico Prediction of Ames Mutagenicity. J. Chem. Inf. Model. 49(9): 2077-2081 (2009) - 2008
- [j11]Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Katja Hansen, Antonius ter Laak
A Probabilistic Approach to Classifying Metabolic Stability. J. Chem. Inf. Model. 48(4): 785-796 (2008) - 2007
- [j10]Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J. Comput. Aided Mol. Des. 21(9): 485-498 (2007) - [j9]Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J. Comput. Aided Mol. Des. 21(12): 651-664 (2007) - [j8]Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Julian Laub, Antonius ter Laak
Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach. J. Chem. Inf. Model. 47(2): 407-424 (2007) - 2005
- [j7]Klaus-Robert Müller
Classifying 'Drug-likeness' with Kernel-Based Learning Methods. J. Chem. Inf. Model. 45(2): 249-253 (2005) - 2004
- [c13]Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf:
A kernel view of the dimensionality reduction of manifolds. ICML 2004 - 2003
- [j6]Sebastian Mika, Gunnar Rätsch
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003) - 2002
- [b1]Sebastian Mika:
Kern Fisher Diskriminaten. Tecnical University of Berlin, Germany, 2002, pp. I-X, 1-144 - [j5]Gunnar Rätsch
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(9): 1184-1199 (2002) - [c12]Gunnar Rätsch, Alexander J. Smola, Sebastian Mika:
Adapting Codes and Embeddings for Polychotomies. NIPS 2002: 513-520 - [p1]Sebastian Mika:
Kern Fisher Diskriminanten. Ausgezeichnete Informatikdissertationen 2002: 69-78 - 2001
- [j4]Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson:
Regularized Principal Manifolds. J. Mach. Learn. Res. 1: 179-209 (2001) - [j3]Klaus-Robert Müller
An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 12(2): 181-201 (2001) - [c11]Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf:
An improved training algorithm for kernel Fisher discriminants. AISTATS 2001: 209-215 - [c10]Koji Tsuda, Gunnar Rätsch
Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers. ICANN 2001: 331-338 - [c9]Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth:
On the Convergence of Leveraging. NIPS 2001: 487-494 - 2000
- [j2]Alexander Zien, Gunnar Rätsch
Engineering support vector machine kernels that recognize translation initiation sites. Bioinform. 16(9): 799-807 (2000) - [c8]Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller:
Barrier Boosting. COLT 2000: 170-179 - [c7]Sebastian Mika, Gunnar Rätsch, Klaus-Robert Müller:
A Mathematical Programming Approach to the Kernel Fisher Algorithm. NIPS 2000: 591-597 - [c6]Gunnar Rätsch
Robust Ensemble Learning for Data Mining. PAKDD 2000: 341-344
1990 – 1999
- 1999
- [j1]Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller
Input space versus feature space in kernel-based methods. IEEE Trans. Neural Networks 10(5): 1000-1017 (1999) - [c5]Alexander J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf:
Regularized Principal Manifolds. EuroCOLT 1999: 214-229 - [c4]Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alexander J. Smola, Thomas Lengauer, Klaus-Robert Müller:
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. German Conference on Bioinformatics 1999: 37-43 - [c3]Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Invariant Feature Extraction and Classification in Kernel Spaces. NIPS 1999: 526-532 - [c2]Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika:
v-Arc: Ensemble Learning in the Presence of Outliers. NIPS 1999: 561-567 - 1998
- [c1]Sebastian Mika, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch:
Kernel PCA and De-Noising in Feature Spaces. NIPS 1998: 536-542
Coauthor Index
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