SVMMaj: SVMMaj algorithm

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Implements the SVM-Maj algorithm to train data with Support Vector Machine, this algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.

Author
Hoksan Yip, Patrick J.F. Groenen, Georgi Nalbantov
Date of publication
2011-01-13 12:09:49
Maintainer
Hok San Yip <hoksan@gmail.com>
License
GPL-2
Version
0.2-2

View on CRAN

Man pages

australian
Australian Credit Approval Dataset
hinge
Hinge error function of SVM-Maj
ispline
I-spline basis of each column of a given matrix
normalize
Normalize/standardize the colums of a matrix
svmmaj
SVM-Maj Algorithm
svmmajcrossval
k-fold Cross-Validation of SVM-Maj
voting
Congressional Voting Records Data Set

Files in this package

SVMMaj
SVMMaj/data
SVMMaj/data/AusCredit.r
SVMMaj/data/diabetes.r
SVMMaj/data/libsvm.Rdata
SVMMaj/data/voting.R
SVMMaj/DESCRIPTION
SVMMaj/man
SVMMaj/man/australian.Rd
SVMMaj/man/diabetes.rd
SVMMaj/man/hinge.Rd
SVMMaj/man/ispline.Rd
SVMMaj/man/normalize.Rd
SVMMaj/man/predict.svmmaj.rd
SVMMaj/man/svmmaj.Rd
SVMMaj/man/svmmajcrossval.Rd
SVMMaj/man/voting.Rd
SVMMaj/NAMESPACE
SVMMaj/R
SVMMaj/R/hinge.R
SVMMaj/R/ispline.R
SVMMaj/R/normalize.R
SVMMaj/R/roccurve.r
SVMMaj/R/svmmaj.R
SVMMaj/R/svmmaj2.r
SVMMaj/R/svmmajcrossval.R
SVMMaj/R/svmmajdefault.R
SVMMaj/R/svmmajtuning.R
SVMMaj/R/update.R