kernlab (version 0.9-24)

inlearn: Onlearn object initialization

Description

Online Kernel Algorithm object onlearn initialization function.

Usage

"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

Value

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

Details

The inlearn is used to initialize a blank onlearn object.

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))

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