Provide a value for lambda
, and produce the fitted lagrange alpha
values. Provide values for x
, and get fitted function values or
class labels.
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object 
fitted 
newx 
values of 
lambda 
the value of the regularization parameter. Note that

type 
type of prediction, with default 
... 
Generic compatibility 
This implementation of the SVM uses a parameterization that is slightly
different but equivalent to the usual (Vapnik) SVM. Here
lambda=1/C.
The Lagrange multipliers are related via
αstar = alpha/lambda, where
alphastar is the usual multiplier, and
alpha our multiplier. Note that if 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.
In each case, the desired prediction.
Trevor Hastie
The paper http://wwwstat.stanford.edu/~hastie/Papers/svmpath.pdf, as well as the talk http://wwwstat.stanford.edu/~hastie/TALKS/svmpathtalk.pdf.
coef.svmpath, svmpath
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