Header logo is

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

1998

Article

ei


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.

Author(s): Smola, AJ. and Schölkopf, B.
Journal: Algorithmica
Volume: 22
Number (issue): 1-2
Pages: 211-231
Year: 1998
Month: September
Day: 0

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

Digital: 0
DOI: 10.1007/PL00013831
Institution: GMD
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@article{948,
  title = {On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion },
  author = {Smola, AJ. and Sch{\"o}lkopf, B.},
  journal = {Algorithmica},
  volume = {22},
  number = {1-2},
  pages = {211-231},
  organization = {Max-Planck-Gesellschaft},
  institution = {GMD},
  school = {Biologische Kybernetik},
  month = sep,
  year = {1998},
  month_numeric = {9}
}