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maboost (version 1.0-0)

Binary and Multiclass Boosting Algorithms

Description

Performs binary and multiclass boosting in maximum-margin, sparse, smooth and normal settings as described in "A Boosting Framework on Grounds of Online Learning" by T. Naghibi and B. Pfister, (2014). For further information regarding the algorithms, please refer to http://arxiv.org/abs/1409.7202

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Version

Install

install.packages('maboost')

Monthly Downloads

11

Version

1.0-0

License

GPL (>= 2)

Maintainer

Tofigh Naghibi

Last Published

November 25th, 2014

Functions in maboost (1.0-0)

varplot.maboost

Variable selection with maboost
print.maboost

Model Information for Ada
summary.maboost

Summary of model fit for arbitrary data (test, validation, or training)
update.maboost

Add more trees to an maboost object
maboost

Binary and Multiclass Boosting Algorithms
predict.maboost

Predict a data set using maboost