\HeaderA{gbmCMA-methods}{Tree-based Gradient Boosting}{gbmCMA.Rdash.methods}
\aliasA{gbmCMA,data.frame,missing,formula-method}{gbmCMA-methods}{gbmCMA,data.frame,missing,formula.Rdash.method}
\aliasA{gbmCMA,ExpressionSet,character,missing-method}{gbmCMA-methods}{gbmCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{gbmCMA,matrix,factor,missing-method}{gbmCMA-methods}{gbmCMA,matrix,factor,missing.Rdash.method}
\aliasA{gbmCMA,matrix,numeric,missing-method}{gbmCMA-methods}{gbmCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{gbmCMA-methods}
\begin{Description}\relax
Roughly speaking, Boosting combines 'weak learners'
in a weighted manner in a stronger ensemble. This
method calls the function \code{gbm.fit} from the
package \code{gbm}. The 'weak learners' are
simple trees that need only very few splits (default: 1).
\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{gbmCMA}{gbmCMA}.}
\end{Section}

