wSVM (0.1-7)

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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.

Maintainer: SungHwan Kim
Author(s): SungHwan Kim and Soo-Heang Eo

License: GPL-2

Uses: MASS, quadprog

Released over 9 years ago.



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