Class "onlearn"
The class of objects used by the Kernel-based Online learning algorithms
Objects can be created by calls of the form new("onlearn", ...).
or by calls to the function inlearn.
kernelf:Object of class "function" containing
the used kernel function
buffer:Object of class "numeric" containing
the size of the buffer
kpar:Object of class "list" containing the
hyperparameters of the kernel function.
xmatrix:Object of class "matrix" containing
the data points (similar to support vectors)
fit:Object of class "numeric" containing the
decision function value of the last data point
onstart:Object of class "numeric" used for indexing
onstop:Object of class "numeric" used for indexing
alpha:Object of class "ANY" containing the
model parameters
rho:Object of class "numeric" containing model
parameter
b:Object of class "numeric" containing the offset
pattern:Object of class "factor" used for
dealing with factors
type:Object of class "character" containing
the problem type (classification, regression, or novelty
signature(object = "onlearn"): returns the model
parameters
signature(object = "onlearn"): returns the offset
signature(object = "onlearn"): returns the
buffer size
signature(object = "onlearn"): returns the last
decision function value
signature(object = "onlearn"): return the
kernel function used
signature(object = "onlearn"): returns the
hyper-parameters used
signature(obj = "onlearn"): the learning function
signature(object = "onlearn"): the predict function
signature(object = "onlearn"): returns model parameter
signature(object = "onlearn"): show function
signature(object = "onlearn"): returns the type
of problem
signature(object = "onlearn"): returns the
stored data points
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
## 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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.