# inlearn: Onlearn object initialization In kernlab: Kernel-Based Machine Learning Lab

## Description

Online Kernel Algorithm object `onlearn` initialization function.

## Usage

 ```1 2 3``` ```## 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.

## Value

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

`onlearn`, `onlearn-class`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## 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)) ```