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\name{ksvm-class}
\docType{class}
\alias{ksvm-class}
\alias{SVindex}
\alias{alphaindex}
\alias{prob.model}
\alias{scaling}
\alias{prior}
\alias{show}
\alias{param}
\alias{b}
\alias{obj}
\alias{nSV}
\alias{coef,vm-method}
\alias{SVindex,ksvm-method}
\alias{alpha,ksvm-method}
\alias{alphaindex,ksvm-method}
\alias{cross,ksvm-method}
\alias{error,ksvm-method}
\alias{param,ksvm-method}
\alias{fitted,ksvm-method}
\alias{prior,ksvm-method}
\alias{prob.model,ksvm-method}
\alias{kernelf,ksvm-method}
\alias{kpar,ksvm-method}
\alias{lev,ksvm-method}
\alias{kcall,ksvm-method}
\alias{scaling,ksvm-method}
\alias{type,ksvm-method}
\alias{xmatrix,ksvm-method}
\alias{ymatrix,ksvm-method}
\alias{b,ksvm-method}
\alias{obj,ksvm-method}
\alias{nSV,ksvm-method}
\title{Class "ksvm" }
\description{An S4 class containing the output (model) of the
\code{ksvm} Support Vector Machines function }
\section{Objects from the Class}{
Objects can be created by calls of the form \code{new("ksvm", ...)}
or by calls to the \code{ksvm} function.
}
\section{Slots}{
\describe{
\item{\code{type}:}{Object of class \code{"character"} containing
the support vector machine type
("C-svc", "nu-svc", "C-bsvc", "spoc-svc",
"one-svc", "eps-svr", "nu-svr", "eps-bsvr")}
\item{\code{param}:}{Object of class \code{"list"} containing the
Support Vector Machine parameters (C, nu, epsilon)}
\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{"ANY"} containing the \code{ksvm} function call}
\item{\code{scaling}:}{Object of class \code{"ANY"} containing the
scaling information performed on the data}
\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"} (\code{"list"}
for multiclass problems
or \code{"matrix"} for binary classification and regression
problems) containing the support vectors calculated from
the data matrix used during computations (possibly scaled and
without NA). In the case of multi-class classification each list
entry contains the support vectors from each binary classification
problem from the one-against-one method.}
\item{\code{ymatrix}:}{Object of class \code{"output"}
the response \code{"matrix"} or \code{"factor"} or \code{"vector"} or
\code{"logical"}}
\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{prob.model}:}{Object of class \code{"list"} with the
class prob. model}
\item{\code{prior}:}{Object of class \code{"list"} with the
prior of the training set}
\item{\code{nclass}:}{Object of class \code{"numeric"} containing
the number of classes (in the case of classification)}
\item{\code{alpha}:}{Object of class \code{"listI"} containing the
resulting alpha vector (\code{"list"} or \code{"matrix"} in case of multiclass classification) (support vectors)}
\item{\code{coef}:}{Object of class \code{"ANY"} containing the
resulting coefficients}
\item{\code{alphaindex}:}{Object of class \code{"list"} containing}
\item{\code{b}:}{Object of class \code{"numeric"} containing the
resulting offset }
\item{\code{SVindex}:}{Object of class \code{"vector"} containing
the indexes of the support vectors}
\item{\code{nSV}:}{Object of class \code{"numeric"} containing the
number of support vectors }
\item{\code{obj}:}{Object of class \code{vector} containing the value of the objective function. When using
one-against-one in multiclass classification this is a vector.}
\item{\code{error}:}{Object of class \code{"numeric"} containing the
training error}
\item{\code{cross}:}{Object of class \code{"numeric"} containing the
cross-validation error }
\item{\code{n.action}:}{Object of class \code{"ANY"} containing the
action performed for NA }
}
}
\section{Methods}{
\describe{
\item{SVindex}{\code{signature(object = "ksvm")}: return the indexes
of support vectors}
\item{alpha}{\code{signature(object = "ksvm")}: returns the complete
5 alpha vector (wit zero values)}
\item{alphaindex}{\code{signature(object = "ksvm")}: returns the
indexes of non-zero alphas (support vectors)}
\item{cross}{\code{signature(object = "ksvm")}: returns the
cross-validation error }
\item{error}{\code{signature(object = "ksvm")}: returns the training
error }
\item{obj}{\code{signature(object = "ksvm")}: returns the value of the objective function}
\item{fitted}{\code{signature(object = "vm")}: returns the fitted
values (predict on training set) }
\item{kernelf}{\code{signature(object = "ksvm")}: returns the kernel
function}
\item{kpar}{\code{signature(object = "ksvm")}: returns the kernel
parameters (hyperparameters)}
\item{lev}{\code{signature(object = "ksvm")}: returns the levels in
case of classification }
\item{prob.model}{\code{signature(object="ksvm")}: returns class
prob. model values}
\item{param}{\code{signature(object="ksvm")}: returns
the parameters of the SVM in a list (C, epsilon, nu etc.)}
\item{prior}{\code{signature(object="ksvm")}: returns
the prior of the training set}
\item{kcall}{\code{signature(object="ksvm")}: returns the
\code{ksvm} function call}
\item{scaling}{\code{signature(object = "ksvm")}: returns the
scaling values }
\item{show}{\code{signature(object = "ksvm")}: prints the object information}
\item{type}{\code{signature(object = "ksvm")}: returns the problem type}
\item{xmatrix}{\code{signature(object = "ksvm")}: returns the data
matrix used}
\item{ymatrix}{\code{signature(object = "ksvm")}: returns the
response vector}
}
}
\author{Alexandros Karatzoglou \cr \email{alexandros.karatzolgou@ci.tuwien.ac.at}}
\seealso{
\code{\link{ksvm}},
\code{\link{rvm-class}},
\code{\link{gausspr-class}}
}
\examples{
## simple example using the promotergene data set
data(promotergene)
## train a support vector machine
gene <- ksvm(Class~.,data=promotergene,kernel="rbfdot",
kpar=list(sigma=0.015),C=50,cross=4)
gene
# the kernel function
kernelf(gene)
# the alpha values
alpha(gene)
# the coefficients
coef(gene)
# the fitted values
fitted(gene)
# the cross validation error
cross(gene)
}
\keyword{classes}
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