@article{ZienRaetschMikaetal.2000,
  author    = {Zien, Alexander and R{\"a}tsch, Gunnar and Mika, Sebastian and Sch{\"o}lkopf, Bernhard and Lengauer, Thomas and M{\"u}ller, Klaus-Robert},
  title     = {Engineering support vector machine kernels that recognize translation initiation sites},
  issn      = {1367-4803},
  year      = {2000},
  language  = {en}
}
@article{RaetschSchoelkopfSmolaetal.2000,
  author    = {R{\"a}tsch, Gunnar and Sch{\"o}lkopf, B. and Smola, Alexander J. and Mika, Sebastian and Onoda, T. and M{\"u}ller, Klaus-Robert},
  title     = {Robust ensemble learning},
  isbn      = {0-262-19448-1},
  year      = {2000},
  language  = {en}
}
@book{RaetschSchoelkopfMikaetal.2000,
  author    = {R{\"a}tsch, Gunnar and Sch{\"o}lkopf, B. and Mika, Sebastian and M{\"u}ller, Klaus-Robert},
  title     = {SVM and boosting : one class},
  series = {GMD-Report},
  volume    = {119},
  journal   = {GMD-Report},
  publisher = {GMD-Forschungszentrum Informationstechnik},
  address   = {Sankt Augustin},
  pages     = {36 S.},
  year      = {2000},
  language  = {en}
}
@article{RaetschSchoelkopfSmolaetal.2000,
  author    = {R{\"a}tsch, Gunnar and Sch{\"o}lkopf, B. and Smola, Alexander J. and M{\"u}ller, Klaus-Robert and Mika, Sebastian},
  title     = {V-Arc : ensemble learning in the preence of outliers},
  year      = {2000},
  language  = {en}
}
@article{MikaRaetschWestonetal.2000,
  author    = {Mika, Sebastian and R{\"a}tsch, Gunnar and Weston, J. and Sch{\"o}lkopf, B. and Smola, Alexander J. and M{\"u}ller, Klaus-Robert},
  title     = {Invariant feature extraction and classification in kernel spaces},
  year      = {2000},
  language  = {en}
}
@article{RaetschSchoelkopfSmolaetal.2000,
  author    = {R{\"a}tsch, Gunnar and Sch{\"o}lkopf, B. and Smola, Alexander J. and Mika, Sebastian and Onoda, T. and M{\"u}ller, Klaus-Robert},
  title     = {Robust ensemble learning for data analysis},
  year      = {2000},
  language  = {en}
}
@article{MullerRatschSonnenburgetal.2005,
  author    = {Muller, K. R. and Ratsch, G. and Sonnenburg, S. and Mika, Sebastian and Grimm, M. and Heinrich, N.},
  title     = {Classifying 'drug-likeness' with kernel-based learning methods},
  issn      = {1549-9596},
  year      = {2005},
  abstract  = {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},
  language  = {en}
}