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\name{onlearn-class}
\docType{class}
\alias{onlearn-class}
\alias{alpha,onlearn-method}
\alias{b,onlearn-method}
\alias{buffer,onlearn-method}
\alias{fit,onlearn-method}
\alias{kernelf,onlearn-method}
\alias{kpar,onlearn-method}
\alias{predict,onlearn-method}
\alias{rho,onlearn-method}
\alias{rho}
\alias{show,onlearn-method}
\alias{type,onlearn-method}
\alias{xmatrix,onlearn-method}
\alias{buffer}
\title{Class "onlearn"}
\description{ The class of objects used by the Kernel-based Online
learning algorithms}
\section{Objects from the Class}{
Objects can be created by calls of the form \code{new("onlearn", ...)}.
or by calls to the function \code{inlearn}.
}
\section{Slots}{
\describe{
\item{\code{kernelf}:}{Object of class \code{"function"} containing
the used kernel function}
\item{\code{buffer}:}{Object of class \code{"numeric"} containing
the size of the buffer}
\item{\code{kpar}:}{Object of class \code{"list"} containing the
hyperparameters of the kernel function.}
\item{\code{xmatrix}:}{Object of class \code{"matrix"} containing
the data points (similar to support vectors) }
\item{\code{fit}:}{Object of class \code{"numeric"} containing the
decision function value of the last data point}
\item{\code{onstart}:}{Object of class \code{"numeric"} used for indexing }
\item{\code{onstop}:}{Object of class \code{"numeric"} used for indexing}
\item{\code{alpha}:}{Object of class \code{"ANY"} containing the
model parameters}
\item{\code{rho}:}{Object of class \code{"numeric"} containing model
parameter}
\item{\code{b}:}{Object of class \code{"numeric"} containing the offset}
\item{\code{pattern}:}{Object of class \code{"factor"} used for
dealing with factors}
\item{\code{type}:}{Object of class \code{"character"} containing
the problem type (classification, regression, or novelty }
}
}
\section{Methods}{
\describe{
\item{alpha}{\code{signature(object = "onlearn")}: returns the model
parameters}
\item{b}{\code{signature(object = "onlearn")}: returns the offset }
\item{buffer}{\code{signature(object = "onlearn")}: returns the
buffer size}
\item{fit}{\code{signature(object = "onlearn")}: returns the last
decision function value}
\item{kernelf}{\code{signature(object = "onlearn")}: return the
kernel function used}
\item{kpar}{\code{signature(object = "onlearn")}: returns the
hyper-parameters used}
\item{onlearn}{\code{signature(obj = "onlearn")}: the learning function}
\item{predict}{\code{signature(object = "onlearn")}: the predict function}
\item{rho}{\code{signature(object = "onlearn")}: returns model parameter}
\item{show}{\code{signature(object = "onlearn")}: show function}
\item{type}{\code{signature(object = "onlearn")}: returns the type
of problem}
\item{xmatrix}{\code{signature(object = "onlearn")}: returns the
stored data points}
}
}
\author{Alexandros Karatzoglou\cr
\email{alexandros.karatzoglou@ci.tuwien.ac.at}}
\seealso{
\code{\link{onlearn}}, \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")
## learn one data point at the time
for(i in sample(1:100,100))
on <- onlearn(on,x[i,],y[i],nu=0.03,lambda=0.1)
sign(predict(on,x))
}
\keyword{classes}
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