kernlab is an extensible package for kernel-based machine learning methods in R. It
takes advantage of R's new S4 object model and provides a framework for creating and
using kernel-based algorithms. The package contains dot product primitives (kernels),
implementations of support vector machines and the relevance vector machine, Gaussian
processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm.
Moreover it provides a general purpose quadratic programming solver, and an
incomplete Cholesky decomposition method.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3999 |
| Date | 11 1900 |
| Creators | Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim |
| Publisher | American Statistical Association |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://www.jstatsoft.org/v11/i09/paper, http://epub.wu.ac.at/3999/ |
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