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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

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

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PostScript


  title = {New Approaches to Statistical Learning Theory},
  author = {Bousquet, O.},
  journal = {Annals of the Institute of Statistical Mathematics},
  volume = {55},
  number = {2},
  pages = {371-389},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2003}