inlearn: Onlearn object initialization

Description

Online Kernel Algorithm object onlearn initialization function.

Usage

## S3 method for class 'numeric':
inlearn(d, kernel = "rbfdot", kpar = list(sigma = 0.1), type = "novelty", 
       buffersize = 1000)

Arguments

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

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 :

sigmainverse kernel width for the Radial B

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.

Examples

Run this code

## 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))