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wSVM (version 0.1-7)

Weighted SVM with boosting algorithm for improving accuracy

Description

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.

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Version

Install

install.packages('wSVM')

Monthly Downloads

13

Version

0.1-7

License

GPL-2

Maintainer

Last Published

October 29th, 2012

Functions in wSVM (0.1-7)

wsvm

Weighted SVM with boosting algorithm for improving accuracy
wsvm.predict

Predict new test set using wsvm function and compute error rate
mixture.example

mixture example
Error.rate

Calculate Error rate
wSVM-package

Weigthed SVM with boosting algorithm for improving accuracy
wsvm.boost

Weighted SVM using boosting algorithm
wsvm.kernel

Compute kernel K(X, U)
simul.wsvm

Generating simulation data for weighted svm