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Constructing boosting algorithms from SVMs: an application to one-class classification
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.
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Self-citationThe Strength of Weak Learnability
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Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Machine learning for internet of things data analysis: a survey
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A Unifying Review of Deep and Shallow Anomaly Detection
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 914
Self-citationNovelty detection: a review—part 2:
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Introduction to machine learning for brain imaging
Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K.-R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.004 · Cited by 630
Metadata
- Authors
- Ratsch, G., Mika, S., Scholkopf, B., & Muller, K.-R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-09
- Volume
- 24
- Issue
- 9
- Pages
- 1184-1199
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2143386126
- Cited by
- 248
- References
- 7
- FWCI
- 11.04
- Type
- journal article
- Language
- en
- Open access
- closed
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