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.

Author | SungHwan Kim and Soo-Heang Eo |

Date of publication | 2012-10-29 08:59:59 |

Maintainer | SungHwan Kim <swiss747@korea.ac.kr> |

License | GPL-2 |

Version | 0.1-7 |

**Error.rate:** Calculate Error rate

**mixture.example:** mixture example

**simul.wsvm:** Generating simulation data for weighted svm

**wsvm:** Weighted SVM with boosting algorithm for improving accuracy

**wsvm.boost:** Weighted SVM using boosting algorithm

**wsvm.kernel:** Compute kernel K(X, U)

**wSVM-package:** Weigthed SVM with boosting algorithm for improving accuracy

**wsvm.predict:** Predict new test set using wsvm function and compute error...

wSVM

wSVM/data

wSVM/data/mixture.example.RData

wSVM/INDEX

wSVM/NAMESPACE

wSVM/man

wSVM/man/wsvm.boost.Rd
wSVM/man/Error.rate.Rd
wSVM/man/simul.wsvm.Rd
wSVM/man/wsvm.Rd
wSVM/man/mixture.example.Rd
wSVM/man/wSVM-package.Rd
wSVM/man/wsvm.kernel.Rd
wSVM/man/wsvm.predict.Rd
wSVM/LICENCE

wSVM/DESCRIPTION

wSVM/MD5

wSVM/R

wSVM/R/wsvm.predict.R
wSVM/R/wsvm.boost.r

wSVM/R/simul.wsvm.r

wSVM/R/Error.rate.r

wSVM/R/wsvm.r

wSVM/R/wsvm.kernel.r

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