inlearn function

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)

Arguments

• 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:

  • rbfdot Radial Basis kernel function "Gaussian"
  • polydot Polynomial kernel function
  • vanilladot Linear kernel function
  • tanhdot Hyperbolic tangent kernel function
  • laplacedot Laplacian kernel function
  • besseldot Bessel kernel function
  • anovadot ANOVA RBF kernel function

  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 :

  • sigma inverse kernel width for the Radial Basis kernel function "rbfdot" and the Laplacian kernel "laplacedot".
  • degree, scale, offset for the Polynomial kernel "polydot"
  • scale, offset for the Hyperbolic tangent kernel function "tanhdot"
  • sigma, order, degree for the Bessel kernel "besseldot".
  • sigma, degree for the ANOVA kernel "anovadot".

  Hyper-parameters for user defined kernels can be passed through the kpar parameter as well.

• type: the type of problem to be learned by the online algorithm : classification, regression, novelty

• buffersize: the size of the buffer to be used

Details

The inlearn is used to initialize a blank onlearn object.

Returns

The function returns an S4 object of class onlearn that can be used by the onlearn function.

Author(s)

Alexandros Karatzoglou

alexandros.karatzoglou@ci.tuwien.ac.at

See Also

onlearn, onlearn-class

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))