Sebastian Mika

According to our database1, Sebastian Mika authored at least 27 papers between 1998 and 2013.

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Bibliography

2013
Kernels, Pre-images and Optimization.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2009
Bias-Correction of Regression Models: A Case Study on hERG Inhibition.
J. Chem. Inf. Model., 2009

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity.
J. Chem. Inf. Model., 2009

2008
A Probabilistic Approach to Classifying Metabolic Stability.
J. Chem. Inf. Model., 2008

2007
Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach.
J. Chem. Inf. Model., 2007

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.
J. Comput. Aided Mol. Des., 2007

2005
Classifying 'Drug-likeness' with Kernel-Based Learning Methods.
J. Chem. Inf. Model., 2005

2004
A kernel view of the dimensionality reduction of manifolds.
Proceedings of the Machine Learning, 2004

2003
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces.
IEEE Trans. Pattern Anal. Mach. Intell., 2003

2002
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification.
IEEE Trans. Pattern Anal. Mach. Intell., 2002

Adapting Codes and Embeddings for Polychotomies.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Kern Fisher Diskriminanten.
Proceedings of the Ausgezeichnete Informatikdissertationen 2002, 2002

Kern Fisher Diskriminaten.
PhD thesis, 2002

2001
An introduction to kernel-based learning algorithms.
IEEE Trans. Neural Networks, 2001

Regularized Principal Manifolds.
J. Mach. Learn. Res., 2001

On the Convergence of Leveraging.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers.
Proceedings of the Artificial Neural Networks, 2001

An improved training algorithm for kernel Fisher discriminants.
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000
Engineering support vector machine kernels that recognize translation initiation sites.
Bioinform., 2000

Robust Ensemble Learning for Data Mining.
Proceedings of the Knowledge Discovery and Data Mining, 2000

A Mathematical Programming Approach to the Kernel Fisher Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Barrier Boosting.
Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28, 2000

1999
Input space versus feature space in kernel-based methods.
IEEE Trans. Neural Networks, 1999

v-Arc: Ensemble Learning in the Presence of Outliers.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Invariant Feature Extraction and Classification in Kernel Spaces.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites.
Proceedings of the German Conference on Bioinformatics, 1999

1998
Kernel PCA and De-Noising in Feature Spaces.
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998


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