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\name{onlearn}
\alias{onlearn}
\alias{onlearn,onlearn-method}
\title{Kernel Online Learning algorithms}
\description{
Online Kernel-based Learning algorithms for classification, novelty
detection, and regression.
}
\usage{
\S4method{onlearn}{onlearn}(obj, x, y = NULL, nu = 0.2, lambda = 1e-04)
}
\arguments{
\item{obj}{\code{obj} an object of class \code{onlearn} created by the
initialization function \code{inlearn} containing the kernel to be
used during learning and the parameters of the
learned model}
\item{x}{vector or matrix containing the data. Factors have
to be numerically coded. If \code{x} is a matrix the code is
run internally one sample at the time.}
\item{y}{the class label in case of classification. Only binary
classification is supported and class labels have to be -1 or +1.
}
\item{nu}{the parameter similarly to the \code{nu} parameter in SVM
bounds the training error.}
\item{lambda}{the learning rate}
}
\details{
The online algorithms are based on a simple stochastic gradient descent
method in feature space.
The state of the algorithm is stored in an object of class
\code{onlearn} and has to be passed to the function at each iteration.
}
\value{
The function returns an \code{S4} object of class \code{onlearn}
containing the model parameters and the last fitted value which can be
retrieved by the accessor method \code{fit}. The value returned in the
classification and novelty detection problem is the decision function
value phi.
The accessor methods \code{alpha} returns the model parameters.
}
\references{ Kivinen J. Smola A.J. Williamson R.C. \cr
\emph{Online Learning with Kernels}\cr
IEEE Transactions on Signal Processing vol. 52, Issue 8, 2004\cr
\url{https://alex.smola.org/papers/2004/KivSmoWil04.pdf}}
\author{Alexandros Karatzoglou\cr
\email{alexandros.karatzoglou@ci.tuwien.ac.at}}
\seealso{\code{\link{inlearn}}}
\examples{
## create toy data set
x <- rbind(matrix(rnorm(100),,2),matrix(rnorm(100)+3,,2))
y <- matrix(c(rep(1,50),rep(-1,50)),,1)
## initialize onlearn object
on <- inlearn(2,kernel="rbfdot",kpar=list(sigma=0.2),
type="classification")
ind <- sample(1:100,100)
## learn one data point at the time
for(i in ind)
on <- onlearn(on,x[i,],y[i],nu=0.03,lambda=0.1)
## or learn all the data
on <- onlearn(on,x[ind,],y[ind],nu=0.03,lambda=0.1)
sign(predict(on,x))
}
\keyword{classif}
\keyword{neural}
\keyword{regression}
\keyword{ts}
|
