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dc.contributor.authorMangasarian, Olvi
dc.date.accessioned2013-01-17T17:16:36Z
dc.date.available2013-01-17T17:16:36Z
dc.date.issued2001
dc.identifier.citation01-05en
dc.identifier.urihttp://digital.library.wisc.edu/1793/64302
dc.description.abstractSupport vector machines (SVMs) have played a key role in broad classes of problems arising in various elds. Much more recently, SVMs have become the tool of choice for problems arising in data classi - cation and mining. This paper emphasizes some recent developments that the author and his colleagues have contributed to such as: gen- eralized SVMs (a very general mathematical programming framework for SVMs), smooth SVMs (a smooth nonlinear equation representation of SVMs solvable by a fast Newton method), Lagrangian SVMs (an unconstrained Lagrangian representation of SVMs leading to an ex- tremely simple iterative scheme capable of solving classi cation prob- lems with millions of points) and reduced SVMs (a rectangular kernel classi er that utilizes as little as 1% of the data).en
dc.subjectdata classificationen
dc.subjectsupport vector machinesen
dc.titleData Mining via Support Vector Machinesen
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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