Minimax support vector machines

dc.citation.bibtexNameinproceedings
dc.citation.journalTitleIEEE Workshop on Statistical Signal Processing (SSP)
dc.citation.locationMadison, Wisconsin
dc.contributor.authorDavenport, Mark A.en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorScott, Clayton D.en_US
dc.date.accessioned2008-08-19T03:03:36Z
dc.date.available2008-08-19T03:03:36Z
dc.date.issued2007-08-01en
dc.description.abstractWe study the problem of designing support vector machine (SVM) classifiers that minimize the maximum of the false alarm and miss rates. This is a natural classification setting in the absence of prior information regarding the relative costs of the two types of errors or true frequency of the two classes in nature. Examining two approaches – one based on shifting the offset of a conventionally trained SVM, the other based on the introduction of class-specific weights – we find that when proper care is taken in selecting the weights, the latter approach significantly outperforms the strategy of shifting the offset. We also find that the magnitude of this improvement depends chiefly on the accuracy of the error estimation step of the training procedure. Furthermore, comparison with the minimax probability machine (MPM) illustrates that our SVM approach can outperform the MPM even when the MPM parameters are set by an oracle.en
dc.description.sponsorshipSupported by NSF, DARPA, AFOSR, ONR, and the Texas Instruments Leadership University Program.en
dc.identifier.citationM. A. Davenport, R. G. Baraniuk and C. D. Scott, "Minimax support vector machines," <i>IEEE Workshop on Statistical Signal Processing (SSP),</i> 2007.*
dc.identifier.doihttp://dx.doi.org/10.1109/SSP.2007.4301335en_US
dc.identifier.urihttps://hdl.handle.net/1911/21680
dc.language.isoengen
dc.titleMinimax support vector machinesen
dc.typeJournal articleen
dc.type.dcmiTexten
dc.type.dcmiTexten_US
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