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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.
Package details 


Author  SungHwan Kim and SooHeang Eo 
Maintainer  SungHwan Kim <swiss747@korea.ac.kr> 
License  GPL2 
Version  0.17 
Package repository  View on CRAN 
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