Online Kernel-based Learning algorithms for classification, novelty detection, and regression.
vector or matrix containing the data. Factors have
to be numerically coded. If
the class label in case of classification. Only binary classification is supported and class labels have to be -1 or +1.
the parameter similarly to the
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
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
The accessor methods
alpha returns the model parameters.
Kivinen J. Smola A.J. Williamson R.C.
Online Learning with Kernels
IEEE Transactions on Signal Processing vol. 52, Issue 8, 2004
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## 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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