DATA MINING
Desktop Survival Guide by Graham Williams |
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R provides the svm in e1071 as an interface to LIBSVM, which provides a very efficient and fast implementation.
library(e1071) iris.svm <- svm(Species ~ ., data=iris, probability=TRUE) plot(iris.svm, iris, Petal.Width ~ Petal.Length, slice = list(Sepal.Width = 3, Sepal.Length = 4)) pred <- predict(iris.svm, iris, probability = TRUE) attr(pred, "prob") # to get the probabilities |
kernlab for kernel learning provides ksvm and is more integrated into R so that different kernels can easily be explored. A new class called kernel is introduced, an kernel functions are objects of this class.