Online Kernelbased Learning algorithms for classification, novelty detection, and regression.
1 2 
obj 

x 
vector or matrix containing the data. Factors have
to be numerically coded. If 
y 
the class label in case of classification. Only binary classification is supported and class labels have to be 1 or +1. 
nu 
the parameter similarly to the 
lambda 
the learning rate 
The online algorithms are based on a simple stochastic gradient descent
method in feature space.
The state of the algorithm is stored in an object of class
onlearn
and has to be passed to the function at each iteration.
The function returns an S4
object of class onlearn
containing the model parameters and the last fitted value which can be
retrieved by the accessor method fit
. The value returned in the
classification and novelty detection problem is the decision function
value phi.
The accessor methods alpha
returns the model parameters.
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
Kivinen J. Smola A.J. Williamson R.C.
Online Learning with Kernels
IEEE Transactions on Signal Processing vol. 52, Issue 8, 2004
http://users.cecs.anu.edu.au/~williams/papers/P172.pdf
inlearn
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## 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")
ind < sample(1:100,100)
## learn one data point at the time
for(i in ind)
on < onlearn(on,x[i,],y[i],nu=0.03,lambda=0.1)
## or learn all the data
on < onlearn(on,x[ind,],y[ind],nu=0.03,lambda=0.1)
sign(predict(on,x))

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