Institute Homepage DE Sign In

Back

Empirical Inference Article 2002

Constructing Boosting algorithms from SVMs: an application to one-class classification.

Thumb ticker sm l1170153
Empirical Inference
Bernhard Schölkopf
  • Director
no image
Empirical Inference
Gunnar Rätsch

We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm—one-class leveraging—starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.

Author(s): Rätsch, G. and Mika, S. and Schölkopf, B. and Müller, K-R.
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 24
Number (issue): 9
Pages: 1184-1199
Year: 2002
Month: September
Day: 0
Bibtex Type: Article (article)
DOI: 10.1109/TPAMI.2002.1033211
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex 

@article{972,
  title = {Constructing Boosting algorithms from SVMs: an application to one-class classification.},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  abstract = {We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm—one-class leveraging—starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.},
  volume = {24},
  number = {9},
  pages = {1184-1199},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2002},
  slug = {972},
  author = {R{\"a}tsch, G. and Mika, S. and Sch{\"o}lkopf, B. and M{\"u}ller, K-R.},
  month_numeric = {9}
}