Onlearn object initialization
Online Kernel Algorithm object onlearn initialization function.
## S4 method for signature 'numeric'
inlearn(d, kernel = "rbfdot", kpar = list(sigma = 0.1),
type = "novelty", buffersize = 1000)d |
the dimensionality of the data to be learned |
kernel |
the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:
The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. |
kpar |
the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. For valid parameters for existing kernels are :
Hyper-parameters for user defined kernels can be passed through the
|
type |
the type of problem to be learned by the online algorithm
:
|
buffersize |
the size of the buffer to be used |
The inlearn is used to initialize a blank onlearn object.
The function returns an S4 object of class onlearn that
can be used by the onlearn function.
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.