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 proplem
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 0.8-1 Index]