de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Smola, A., & Schölkopf, B. (1998). On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Algorithmica, 22(1-2), 211-231. doi:10.1007/PL00013831.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E7F5-5
Abstract
We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above are formulated as constrained optimization problems. Previous approaches such as ridge regression, support vector methods, and regularization networks are included as special cases. We show connections between the cost function and some properties up to now believed to apply to support vector machines only. For appropriately chosen cost functions, the optimal solution of all the problems described above can be found by solving a simple quadratic programming problem.