Empirical Inference Article 2003

New Approaches to Statistical Learning Theory

We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms.

Author(s): Bousquet, O.
Journal: Annals of the Institute of Statistical Mathematics
Volume: 55
Number (issue): 2
Pages: 371-389
Year: 2003
Day: 0
Bibtex Type: Article (article)
Digital: 0
Electronic Archiving: grant_archive
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex

@article{1996,
  title = {New Approaches to Statistical Learning Theory},
  journal = {Annals of the Institute of Statistical Mathematics},
  abstract = {We present new tools from probability theory that can be applied to
  the analysis of learning algorithms. These tools allow to derive new
  bounds on the generalization performance of learning algorithms and to
  propose alternative measures of the complexity of the learning task,
  which in turn can be used to derive new learning algorithms.},
  volume = {55},
  number = {2},
  pages = {371-389},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2003},
  slug = {1996},
  author = {Bousquet, O.}
}