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\name{vm-class}
\docType{class}
\alias{vm-class}
\alias{cross}
\alias{alpha}
\alias{error}
\alias{type}
\alias{kernelf}
\alias{xmatrix}
\alias{ymatrix}
\alias{lev}
\alias{kcall}
\alias{alpha,vm-method}
\alias{cross,vm-method}
\alias{error,vm-method}
\alias{fitted,vm-method}
\alias{kernelf,vm-method}
\alias{kpar,vm-method}
\alias{lev,vm-method}
\alias{kcall,vm-method}
\alias{type,vm-method}
\alias{xmatrix,vm-method}
\alias{ymatrix,vm-method}
\title{Class "vm" }
\description{An S4 VIRTUAL class used as a base for the various vector
machine classes in \pkg{kernlab}}
\section{Objects from the Class}{
Objects from the class cannot be created directly but only contained
in other classes.
}
\section{Slots}{
\describe{
\item{\code{alpha}:}{Object of class \code{"listI"} containing the
resulting alpha vector (list in case of multiclass classification) (support vectors)}
\item{\code{type}:}{Object of class \code{"character"} containing
the vector machine type e.g.,
("C-svc", "nu-svc", "C-bsvc", "spoc-svc",
"one-svc", "eps-svr", "nu-svr", "eps-bsvr")}
\item{\code{kernelf}:}{Object of class \code{"function"} containing
the kernel function}
\item{\code{kpar}:}{Object of class \code{"list"} containing the
kernel function parameters (hyperparameters)}
\item{\code{kcall}:}{Object of class \code{"call"} containing the function call}
\item{\code{terms}:}{Object of class \code{"ANY"} containing the
terms representation of the symbolic model used (when using a formula)}
\item{\code{xmatrix}:}{Object of class \code{"input"} the data
matrix used during computations (support vectors) (possibly scaled and without NA)}
\item{\code{ymatrix}:}{Object of class \code{"output"} the response matrix/vector }
\item{\code{fitted}:}{Object of class \code{"output"} with the fitted values,
predictions using the training set.}
\item{\code{lev}:}{Object of class \code{"vector"} with the levels of the
response (in the case of classification)}
\item{\code{nclass}:}{Object of class \code{"numeric"} containing
the number of classes (in the case of classification)}
\item{\code{error}:}{Object of class \code{"vector"} containing the
training error}
\item{\code{cross}:}{Object of class \code{"vector"} containing the
cross-validation error }
\item{\code{n.action}:}{Object of class \code{"ANY"} containing the
action performed for NA }
}
}
\section{Methods}{
\describe{
\item{alpha}{\code{signature(object = "vm")}: returns the complete
alpha vector (wit zero values)}
\item{cross}{\code{signature(object = "vm")}: returns the
cross-validation error }
\item{error}{\code{signature(object = "vm")}: returns the training
error }
\item{fitted}{\code{signature(object = "vm")}: returns the fitted
values (predict on training set) }
\item{kernelf}{\code{signature(object = "vm")}: returns the kernel
function}
\item{kpar}{\code{signature(object = "vm")}: returns the kernel
parameters (hyperparameters)}
\item{lev}{\code{signature(object = "vm")}: returns the levels in
case of classification }
\item{kcall}{\code{signature(object="vm")}: returns the function call}
\item{type}{\code{signature(object = "vm")}: returns the problem type}
\item{xmatrix}{\code{signature(object = "vm")}: returns the data
matrix used(support vectors)}
\item{ymatrix}{\code{signature(object = "vm")}: returns the
response vector}
}
}
\author{Alexandros Karatzoglou \cr \email{alexandros.karatzolgou@ci.tuwien.ac.at}}
\seealso{
\code{\link{ksvm-class}},
\code{\link{rvm-class}},
\code{\link{gausspr-class}}
}
\keyword{classes}
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