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

Raetsch, Gunnar; Mika, Sebastian; Schoelkopf, Bernhard; Mueller, Klaus-Robert

Description

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,...[Show more]

dc.contributor.authorRaetsch, Gunnar
dc.contributor.authorMika, Sebastian
dc.contributor.authorSchoelkopf, Bernhard
dc.contributor.authorMueller, Klaus-Robert
dc.date.accessioned2015-12-13T22:30:48Z
dc.date.available2015-12-13T22:30:48Z
dc.identifier.issn0162-8828
dc.identifier.urihttp://hdl.handle.net/1885/75003
dc.description.abstractWe 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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.sourceIEEE Transactions on Pattern Analysis and Machine Intelligence
dc.subjectKeywords: Support vector machines (SVM); Algorithms; Computer simulation; Constraint theory; Neural networks; Learning systems Boosting; Novelty detection; One-class classification; SVMs; Unsupervised learning
dc.titleConstructing boosting algorithms from SVMs: an application to one-class classification
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume24
dc.date.issued2002
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub4418
local.type.statusPublished Version
local.contributor.affiliationRaetsch, Gunnar, College of Engineering and Computer Science, ANU
local.contributor.affiliationMika, Sebastian, GMD First
local.contributor.affiliationSchoelkopf, Bernhard, Max Planck Institute for Biological Cybernetics
local.contributor.affiliationMueller, Klaus-Robert, University of Potsdam
local.bibliographicCitation.issue9
local.bibliographicCitation.startpage1184
local.bibliographicCitation.lastpage1199
local.identifier.doi10.1109/TPAMI.2002.1033211
dc.date.updated2015-12-11T08:56:45Z
local.identifier.scopusID2-s2.0-0036709275
CollectionsANU Research Publications

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