inlearn {kernlab} | R Documentation

## Onlearn object initialization

### Description

Online Kernel Algorithm object `onlearn` initialization function.

### Usage

    ## 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

### 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))

[Package kernlab version 0.8-1 Index]