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.
| Original language | English |
|---|---|
| Pages (from-to) | 1184-1199 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 24 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2002 Sep 1 |
| Externally published | Yes |
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Keywords
- Boosting
- Novelty detection
- One-class classification
- SVMs
- Unsupervised learning
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Artificial Intelligence
- Computer Vision and Pattern Recognition
Cite this
Constructing boosting algorithms from SVMs : An application to one-class classification. / Rätsch, Gunnar; Mika, Sebastian; Schölkopf, Bernhard; Muller, Klaus.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 9, 01.09.2002, p. 1184-1199.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Constructing boosting algorithms from SVMs
T2 - An application to one-class classification
AU - Rätsch, Gunnar
AU - Mika, Sebastian
AU - Schölkopf, Bernhard
AU - Muller, Klaus
PY - 2002/9/1
Y1 - 2002/9/1
N2 - 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.
AB - 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.
KW - Boosting
KW - Novelty detection
KW - One-class classification
KW - SVMs
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=0036709275&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036709275&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2002.1033211
DO - 10.1109/TPAMI.2002.1033211
M3 - Article
AN - SCOPUS:0036709275
VL - 24
SP - 1184
EP - 1199
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
SN - 0162-8828
IS - 9
ER -