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Hi, I posted some days ago a question concerning the computation of lambda in the smooth.spline function (which I repreat at the bottom of the mail) given _df_ . Unfortunately the documentation is not clear to me. Maybee someone can help to answer in my view the basic question: If the penalized log likelihood is L = (y - f)' W (y - f) + lambda c' Sigma c how the _lambda_ in the above

hi, I am using R's "kernlab" package, exactly i am doing classification using ksvm(.) and predict.ksvm(.).I want use of custom kernel. I am getting some error. # Following R code works (with promotergene dataset): library("kernlab") s <- function(x, y) { sum((x*y)^1.25) } class(s) <- "kernel" data("promotergene") gene <- ksvm(Class ~ .,