Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
[test by reto]
Authors: Alexandros Karatzoglou, Alexandros Smola, Kurt Hornik, Achim Zeileis
Title: kernlab - An S4 Package for Kernel Methods in R
Abstract: kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject 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.

Page views:: 29018. Submitted: 2004-08-15. Published: 2004-11-02.
Paper: kernlab - An S4 Package for Kernel Methods in R     Download PDF (Downloads: 26359)
Supplements:
kernlab_0.4-1.tar.gz: R source package Download (Downloads: 1803; 855KB)
DOI: 10.18637/jss.v011.i09

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.