| onlearn-class {kernlab} | R Documentation |
The class of objects used by the Kernel-based Online learning algorithms
Objects can be created by calls of the form new("onlearn", ...).
or by calls to the function inlearn.
kernelf:"function" containing
the used kernel functionbuffer:"numeric" containing
the size of the bufferkpar:"list" containing the
hyperparameters of the kernel function.xmatrix:"matrix" containing
the data points (similar to support vectors) fit:"numeric" containing the
decision function value of the last data pointonstart:"numeric" used for indexing onstop:"numeric" used for indexingalpha:"ANY" containing the
model parametersrho:"numeric" containing model
parameterb:"numeric" containing the offsetpattern:"factor" used for
dealing with factorstype:"character" containing
the problem type (classification, regression, or novelty signature(object = "onlearn"): returns the model
parameterssignature(object = "onlearn"): returns the offset signature(object = "onlearn"): returns the
buffer sizesignature(object = "onlearn"): returns the last
decision function valuesignature(object = "onlearn"): return the
kernel function usedsignature(object = "onlearn"): returns the
hyper-parameters usedsignature(obj = "onlearn"): the learning functionsignature(object = "onlearn"): the predict functionsignature(object = "onlearn"): returns model parametersignature(object = "onlearn"): show functionsignature(object = "onlearn"): returns the type
of proplemsignature(object = "onlearn"): returns the
stored data points
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
## 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))