onlearn-class {kernlab} | R Documentation

## Class "onlearn"

### Description

The class of objects used by the Kernel-based Online learning algorithms

### Objects from the Class

Objects can be created by calls of the form `new("onlearn", ...)`. or by calls to the function `inlearn`.

### Slots

`kernelf`:

  Object of class `"function"` containing the used kernel function

`buffer`:

  Object of class `"numeric"` containing the size of the buffer

`kpar`:

  Object of class `"list"` containing the hyperparameters of the kernel function.

`xmatrix`:

  Object of class `"matrix"` containing the data points (similar to support vectors)

`fit`:

  Object of class `"numeric"` containing the decision function value of the last data point

`onstart`:

  Object of class `"numeric"` used for indexing

`onstop`:

  Object of class `"numeric"` used for indexing

`alpha`:

  Object of class `"ANY"` containing the model parameters

`rho`:

  Object of class `"numeric"` containing model parameter

`b`:

  Object of class `"numeric"` containing the offset

`pattern`:

  Object of class `"factor"` used for dealing with factors

`type`:

  Object of class `"character"` containing the problem type (classification, regression, or novelty

### Methods

alpha

  `signature(object = "onlearn")`: returns the model parameters

b

  `signature(object = "onlearn")`: returns the offset

buffer

  `signature(object = "onlearn")`: returns the buffer size

fit

  `signature(object = "onlearn")`: returns the last decision function value

kernelf

  `signature(object = "onlearn")`: return the kernel function used

kpar

  `signature(object = "onlearn")`: returns the hyper-parameters used

onlearn

  `signature(obj = "onlearn")`: the learning function

predict

  `signature(object = "onlearn")`: the predict function

rho

  `signature(object = "onlearn")`: returns model parameter

show

  `signature(object = "onlearn")`: show function

type

  `signature(object = "onlearn")`: returns the type of problem

xmatrix

  `signature(object = "onlearn")`: returns the stored data points

### Author(s)

Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at

### See Also

`onlearn`, `inlearn`

### 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 0.9-27 Index]