  Boosting is a popular machine learning algorithm in regression and
classification problems. Boosting can combine a sequence of regression trees to
obtain accurate prediction. In the presence of outliers, traditional boosting,
based on optimizing convex loss functions, may show inferior results. In this
article, a unified robust boosting is proposed for more resistant estimation.
The method utilizes a recently developed concave-convex family for robust
estimation, composite optimization by conjugation operator, and functional
decent boosting. As a result, an iteratively reweighted boosting algorithm can
be conveniently constructed with existing software. Applications in robust
regression, classification and Poisson regression are demonstrated in the R
package ccboost.
