\HeaderA{compBoostCMA-methods}{Componentwise Boosting}{compBoostCMA.Rdash.methods}
\aliasA{compBoostCMA,data.frame,missing,formula-method}{compBoostCMA-methods}{compBoostCMA,data.frame,missing,formula.Rdash.method}
\aliasA{compBoostCMA,ExpressionSet,character,missing-method}{compBoostCMA-methods}{compBoostCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{compBoostCMA,matrix,factor,missing-method}{compBoostCMA-methods}{compBoostCMA,matrix,factor,missing.Rdash.method}
\aliasA{compBoostCMA,matrix,numeric,missing-method}{compBoostCMA-methods}{compBoostCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{compBoostCMA-methods}
\begin{Description}\relax
Roughly speaking, Boosting combines 'weak learners'
in a weighted manner in a stronger ensemble.

'Weak learners' here consist of linear functions
in one component (variable), as proposed by
Buehlmann and Yu (2003).

It also generates sparsity and can as well be as
used for variable selection alone. (s. \code{\LinkA{GeneSelection}{GeneSelection}}.)
\end{Description}
\begin{Section}{Methods}
\describe{

\item[X = "matrix", y = "numeric", f = "missing"] signature 1

\item[X = "matrix", y = "factor", f = "missing"] signature 2

\item[X = "data.frame", y = "missing", f = "formula"] signature 3

\item[X = "ExpressionSet", y = "character", f = "missing"] signature 4
}
For further argument and output information, consult
\code{\LinkA{compBoostCMA}{compBoostCMA}.}
\end{Section}

