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Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten




We describe recent developments and results of statistical learning theory. In the framework of learning from examples, two factors control generalization ability: explaining the training data by a learning machine of a suitable complexity. We describe kernel algorithms in feature spaces as elegant and efficient methods of realizing such machines. Examples thereof are Support Vector Machines (SVM) and Kernel PCA (Principal Component Analysis). More important than any individual example of a kernel algorithm, however, is the insight that any algorithm that can be cast in terms of dot products can be generalized to a nonlinear setting using kernels. Finally, we illustrate the significance of kernel algorithms by briefly describing industrial and academic applications, including ones where we obtained benchmark record results.

Author(s): Schölkopf, B. and Müller, K-R. and Smola, AJ.
Journal: Informatik - Forschung und Entwicklung
Volume: 14
Number (issue): 3
Pages: 154-163
Year: 1999
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1007/s004500050135
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten},
  author = {Sch{\"o}lkopf, B. and M{\"u}ller, K-R. and Smola, AJ.},
  journal = {Informatik - Forschung und Entwicklung},
  volume = {14},
  number = {3},
  pages = {154-163},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {1999},
  month_numeric = {9}