wSVM: Weighted SVM with boosting algorithm for improving accuracy

We propose weighted SVM methods with penalization form. By adding weights to loss term, we can build up weighted SVM easily and examine classification algorithm properties under weighted SVM. Through comparing each of test error rates, we conclude that our Weighted SVM with boosting has predominant properties than the standard SVM have, as a whole.

AuthorSungHwan Kim and Soo-Heang Eo
Date of publication2012-10-29 08:59:59
MaintainerSungHwan Kim <swiss747@korea.ac.kr>
LicenseGPL-2
Version0.1-7

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Files

wSVM
wSVM/data
wSVM/data/mixture.example.RData
wSVM/INDEX
wSVM/NAMESPACE
wSVM/man
wSVM/man/wsvm.boost.Rd wSVM/man/Error.rate.Rd wSVM/man/simul.wsvm.Rd wSVM/man/wsvm.Rd wSVM/man/mixture.example.Rd wSVM/man/wSVM-package.Rd wSVM/man/wsvm.kernel.Rd wSVM/man/wsvm.predict.Rd
wSVM/LICENCE
wSVM/DESCRIPTION
wSVM/MD5
wSVM/R
wSVM/R/wsvm.predict.R
wSVM/R/wsvm.boost.r
wSVM/R/simul.wsvm.r
wSVM/R/Error.rate.r
wSVM/R/wsvm.r
wSVM/R/wsvm.kernel.r

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