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.

Author
SungHwan Kim and Soo-Heang Eo
Date of publication
2012-10-29 08:59:59
Maintainer
SungHwan Kim <swiss747@korea.ac.kr>
License
GPL-2
Version
0.1-7

View on CRAN

Man pages

Error.rate
Calculate Error rate
mixture.example
mixture example
simul.wsvm
Generating simulation data for weighted svm
wsvm
Weighted SVM with boosting algorithm for improving accuracy
wsvm.boost
Weighted SVM using boosting algorithm
wsvm.kernel
Compute kernel K(X, U)
wSVM-package
Weigthed SVM with boosting algorithm for improving accuracy
wsvm.predict
Predict new test set using wsvm function and compute error...

Files in this package

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