inlearn

0th

Percentile

Onlearn object initialization

Online Kernel Algorithm object onlearn initialization function.

Keywords
regression, ts, classif, neural
Usage
# S4 method for 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.

See Also

onlearn, onlearn-class

Aliases
  • inlearn
  • inlearn,numeric-method
Examples
# NOT RUN {
## 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))

# }
Documentation reproduced from package kernlab, version 0.9-27, License:

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