R: Is there an alternative SVM implementation than libsvm in e1071 package?
libsvm, r, svm
Solution
Check the kernlab package:
kernlab: Kernel-based Machine Learning Lab
Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.
Kernlabs `ksvm` supports C-svc, nu-svc, (classification) one-class-svc (novelty) eps-svr, nu-svr (regression) formulations along with native multi-class classification formulations and the bound-constraint SVM formulations. `ksvm` also supports class-probabilities output and confidence intervals for regression.
An interface to the `SVMlight` implementation is provided in package klaR
See also the CRAN Task View Machine Learning & Statistical Learning
Problem
I try to compare different implementations of SVMs in R. Is there another one than the libsvm implementation in the e1071 package ? Generally, is there a good alternative from libsvm which implements the nu-SVM and epsilon-SVM ?