TY  - JOUR
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Mika, Sebastian
A1  - Onoda, T.
A1  - Müller, Klaus-Robert
T1  - Robust ensemble learning for data analysis
Y1  - 2000
ER  - 
TY  - JOUR
A1  - Mika, Sebastian
A1  - Rätsch, Gunnar
A1  - Weston, J.
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Müller, Klaus-Robert
T1  - Invariant feature extraction and classification in kernel spaces
Y1  - 2000
ER  - 
TY  - BOOK
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Mika, Sebastian
A1  - Müller, Klaus-Robert
T1  - SVM and boosting : one class
T3  - GMD-Report
Y1  - 2000
VL  - 119
PB  - GMD-Forschungszentrum Informationstechnik
CY  - Sankt Augustin
ER  - 
TY  - JOUR
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Müller, Klaus-Robert
A1  - Mika, Sebastian
T1  - V-Arc : ensemble learning in the preence of outliers
Y1  - 2000
ER  - 
TY  - JOUR
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Mika, Sebastian
A1  - Onoda, T.
A1  - Müller, Klaus-Robert
T1  - Robust ensemble learning
Y1  - 2000
SN  - 0-262-19448-1
ER  - 
TY  - JOUR
A1  - Zien, Alexander
A1  - Rätsch, Gunnar
A1  - Mika, Sebastian
A1  - Schölkopf, Bernhard
A1  - Lengauer, Thomas
A1  - Müller, Klaus-Robert
T1  - Engineering support vector machine kernels that recognize translation initiation sites
Y1  - 2000
SN  - 1367-4803
ER  - 
TY  - JOUR
A1  - Muller, K. R.
A1  - Ratsch, G.
A1  - Sonnenburg, S.
A1  - Mika, Sebastian
A1  - Grimm, M.
A1  - Heinrich, N.
T1  - Classifying 'drug-likeness' with kernel-based learning methods
N2  - 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 given set of descriptors of the Substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process
Y1  - 2005
SN  - 1549-9596
ER  -