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dc.contributor.authorWild, Edward
dc.contributor.authorMangasarian, Olvi
dc.date.accessioned2013-01-17T17:54:43Z
dc.date.available2013-01-17T17:54:43Z
dc.date.issued2005
dc.identifier.citation05-02en
dc.identifier.urihttp://digital.library.wisc.edu/1793/64330
dc.description.abstractThe multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear kernel as the minimization of a linear function in a finite dimensional (noninteger) real space subject to linear and bilinear constraints. A linearization algorithm is proposed that solves a succession of fast linear programs that converges in a few iterations to a local solution. Computational results on a number of datasets indicate that the proposed algorithm is competitive with the considerably more complex integer programming and other formulations. A distinguishing aspect of our linear classifier not shared by other multiple instance classifiers is the sparse number of features it utilizes. In some tasks the reduction amounts to less than one percent of the original features.en
dc.subjectsuccessive linearization algorithmen
dc.subjectsupport vector machinesen
dc.subjectmultiple instance learningen
dc.titleMultiple Instance Classification via Successive Linear Programmingen
dc.typeTechnical Reporten


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    DMI Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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