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dc.contributor.authorEvgeniou, Theodorosen_US
dc.contributor.authorPontil, Massimilianoen_US
dc.contributor.authorPoggio, Tomasoen_US
dc.date.accessioned2004-10-20T21:04:32Z
dc.date.available2004-10-20T21:04:32Z
dc.date.issued1999-03-01en_US
dc.identifier.otherAIM-1654en_US
dc.identifier.otherCBCL-171en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7261
dc.description.abstractRegularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.en_US
dc.format.extent1526865 bytes
dc.format.extent959195 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-1654en_US
dc.relation.ispartofseriesCBCL-171en_US
dc.titleA Unified Framework for Regularization Networks and Support Vector Machinesen_US


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