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.

Version: 0.1-7
Depends: R (≥ 2.10.1), MASS, quadprog
Published: 2012-10-29
Author: SungHwan Kim and Soo-Heang Eo
Maintainer: SungHwan Kim <swiss747 at>
License: GPL-2 (see file LICENCE)
NeedsCompilation: no
CRAN checks: wSVM results


Reference manual: wSVM.pdf
Package source: wSVM_0.1-7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: wSVM_0.1-7.tgz, r-oldrel: wSVM_0.1-7.tgz
OS X Mavericks binaries: r-release: wSVM_0.1-7.tgz