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.
|Depends:||R (≥ 2.10.1), MASS, quadprog|
|Author:||SungHwan Kim and Soo-Heang Eo|
|Maintainer:||SungHwan Kim <swiss747 at korea.ac.kr>|
|License:||GPL-2 (see file LICENCE)|
|CRAN checks:||wSVM results|
|Windows binaries:||r-devel: wSVM_0.1-7.zip, r-release: wSVM_0.1-7.zip, r-oldrel: wSVM_0.1-7.zip|
|OS X Mavericks binaries:||r-release: wSVM_0.1-7.tgz, r-oldrel: wSVM_0.1-7.tgz|
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