A benchmark data set for in silico prediction of ames mutagenicity (2009)
Hansen, K, Mika, S, Schroeter, T, Sutter, A, Ter Laak, A, Steger-Hartmann, T, ...
No abstract available.
How wrong can we get? A review of machine learning approaches and error bars (2009)
Schwaighofer, A., Schroeter, T., Mika, S., Blanchard, G.
A large number of different machine learning methods can potentially be used for ligand-based virtual screening. In our contribution, we focus on three specific nonlinear methods, namely support...
A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
A. Zien, G. Rätsch, S. Mika, B. Schölkopf, C. Lemmen, A. Smola
Abstract In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding proteins start, the so-called translation initiation...
A probabilistic approach to classifying metabolic stability (2008)
Schwaighofer, A., Schröter, T., Mika, S., Hansen, K., Laak, A. Ter, Lienau, P., ...
Metabolic stability is an important property of drug molecules that should-optimally-be taken into account early on in the drug design process. Along with numerous medium- or high-throughput assays...
Robust Ensemble Learning for Data Mining G. Ratsch 1, B. Scholkopf 2 (2007)
A. J. Smola, S. Mika, T. Onoda
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Accurate solubility prediction with error bars for electrolytes: A machine learning approach (2007)
Schwaighofer, A., Schroeter, T., Mika, S., Laub, J., Laak, A. Ter, Sülzle, D., ...
Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility...
Machine learning models for lipophilicity and their domain of applicability (2007)
Schroeter, T., Schwaighofer, A., Mika, S., Laak, A. Ter, Sülzle, D., Ganzer, U., ...
Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting...
Schroeter, T.S., Schwaighofer, A., Mika, S., Laak, A. Ter, Suelzle, D., Ganzer, U., ...
We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery...
Classifying "drug-likeness" with kernel-based learning methods. (2005)
Müller, Klaus-Robert, Rätsch, Gunnar, Sonnenburg, Sören, Mika, S., Grimm, M., Heinrich, N.
Classifying 'drug-likeness' with kernel-based learning methods (2005)
Rätsch, G., Sonnenburg, S., Mika, S., Grimm, M., Heinrich, N.
In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a...
A kernel view of the dimensionality reduction of manifolds (2004)
Ham,J., Lee,D.D., Mika,S., Schölkopf,B.
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local...
A kernel view of the dimensionality reduction of manifolds (2004)
Ham, J., Lee, D.D., Mika, S., Schölkopf, B., Greiner, R., Schuurmans, D.
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local...
Mika,S., Rätsch,G., Weston,J., Schölkopf,B., Smola,A.J.
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh...
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A.J.
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh...
Mika, S., Ratsch, G., Weston, J., Schölkopf, B., Smola, A.
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh...
Constructing boosting algorithms from SVMs: An application to one-class classification (2002)
Ratsch, G., Mika, S., Schölkopf, B.
We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation...
On the convergence of leveraging (2001)
Rätsch, G., Mika, S., Warmuth, M.
We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logistic Regression and the Least-Square-Boost algorithm for regression. These methods have in common...
On the convergence of leveraging (2001)
Rätsch, G., Mika, S., Warmuth, M.
We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logistic Regression and the Least-Square-Boost algorithm for regression. These methods have in common...
An Introduction to Kernel-Based Learning Algorithms (2001)
Mika, S., Rätsch, G., Tsuda, K., Schoelkopf, B.
This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and kernel principal component analysis (PCA), as examples for successful kernel-based...
Regularized Principal Manifolds (2001)
Smola, A.J., Mika, S., Schoelkopf, B., Williamson, R.C.
Many settings of unsupervised learning can be viewed as quantization problems - the minimization of the expected quantization error subject to some restrictions. This allows the use of tools such as...
Learning to predict the leave-one-out error of kernel based classifiers (2001)
Tsuda, K., Rätsch, G., Mika, S.
We propose an algorithm to predict the leave-one-out (LOO) error for kernel based classifiers. To achieve this goal with computational efficiency, we cast the LOO error approximation task into a...
SVM and boosting. One class (2000)
Rätsch, G., Schölkopf, B., Mika, S.
We show via an equivalence of mathematical programs that a Support Vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation...
An improved training algorithm for kernel fisher discriminants (2000)
Mika, S., Smola, A., Schölkopf, B.
We present a fast training algorithm for the kernel Fisher discriminant classifier. It uses a greedy approximation technique and has an empirical scaling behavior which improves upon the state of the...
Invariant feature extraction and classification in kernel spaces (2000)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A.J.
Engineering support vector machine kernels that recognize translation initiation sites (2000)
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T.
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
SVM and boosting. One class (2000)
Rätsch, G., Schölkopf, B., Mika, S.
We show via an equivalence of mathematical programs that a Support Vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation...
arc ensemble learning in the presence of outliers (2000)
G. Ratsch, B. Scholkopf, A. Smola, T. Onoda, S. Mika
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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites (2000)
A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
Robust Ensemble Learning for Data Analysis (2000)
Gunnar Rätsch, Bernhard Scholkopf, Alex Smola, Sebastian Mika, S. Mika, Klaus-Robert Müller, ...
Classification tasks appearing very often in data analysis and are important sub-tasks in Data Mining. AdaBoost and other Ensemble methods have successfully been applied to a number of classification...
Engineering support vector machine kernels that recognize translation initiation sites (2000)
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T.
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lemmen, C., Smola, A.J., ...
Kernel PCA and de-noising in feature spaces (1999)
Mika, S., Schölkopf, B., Smola, A.J., Scholz, M., Rätsch, G.
Input space vs. feature space in kernel-based methods (1999)
Schölkopf, B., Mika, S., Burges, C.J., Knirsch, P., Rätsch, G., ...