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\name{couple}
\alias{couple}
\title{Probabilities Coupling function}
\description{
\code{couple} is used to link class-probability estimates produced by
pairwise coupling in multi-class classification problems.
}
\usage{
couple(probin, coupler = "minpair")
}
\arguments{
\item{probin}{ The pairwise coupled class-probability estimates}
\item{coupler}{The type of coupler to use. Currently \code{minpar} and
\code{pkpd} and \code{vote} are supported (see reference for more
details).
If \code{vote} is selected the returned value is a primitive estimate
passed on given votes.}
}
\details{
As binary classification problems are much easier to solve many
techniques exist to decompose multi-class classification problems into
many binary classification problems (voting, error codes,
etc.). Pairwise coupling (one against one) constructs a rule for
discriminating between every pair of classes and then selecting the
class
with the most winning two-class decisions.
By using Platt's probabilities output for SVM one can get a class
probability for each of the \eqn{k(k-1)/2} models created in the pairwise
classification. The couple method implements various techniques to combine
these probabilities.
}
\value{
A matrix with the resulting probability estimates.
}
\references{
Ting-Fan Wu, Chih-Jen Lin, ruby C. Weng\cr
\emph{Probability Estimates for Multi-class Classification by Pairwise
Coupling}\cr
Neural Information Processing Symposium 2003 \cr
\url{https://papers.neurips.cc/paper/2454-probability-estimates-for-multi-class-classification-by-pairwise-coupling.pdf}
}
\author{Alexandros Karatzoglou \cr \email{alexandros.karatzoglou@ci.tuwien.ac.at} }
\seealso{ \code{\link{predict.ksvm}}, \code{\link{ksvm}}}
\examples{
## create artificial pairwise probabilities
pairs <- matrix(c(0.82,0.12,0.76,0.1,0.9,0.05),2)
couple(pairs)
couple(pairs, coupler="pkpd")
couple(pairs, coupler ="vote")
}
\keyword{classif}
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