onlearn-class function

Class "onlearn"

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

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))