
Provide a value for lambda
, and produce the fitted lagrange alpha
values. Provide values for x
, and get fitted function values or
class labels.
# S3 method for svmpath
predict(object, newx, lambda, type = c("function", "class",
"alpha", "margin"),...)
fitted svmpath
object
values of x
at which prediction are wanted. This is a
matrix with observations per row
the value of the regularization parameter. Note that
lambda
is equivalent to 1/C for the usual parametrization of
a SVM
type of prediction, with default "function"
. For
type="alpha"
or type="margin"
the newx
argument is not required
Generic compatibility
In each case, the desired prediction.
This implementation of the SVM uses a parameterization that is slightly
different but equivalent to the usual (Vapnik) SVM. Here
alpha=0
, that
observation is right of the elbow; alpha=1
, left of the elbow;
0<alpha<1
on the elbow. The latter two cases are all support
points.
The paper http://www-stat.stanford.edu/~hastie/Papers/svmpath.pdf, as well as the talk http://www-stat.stanford.edu/~hastie/TALKS/svmpathtalk.pdf.
coef.svmpath, svmpath
# NOT RUN {
data(svmpath)
attach(balanced.overlap)
fit <- svmpath(x,y,trace=TRUE,plot=TRUE)
predict(fit, lambda=1,type="alpha")
predict(fit, x, lambda=.9)
detach(2)
# }
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