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