A service provided by the WU Library and the WU IT-Services

kernlab - An S4 Package for Kernel Methods in R

Zeileis, Achim and Hornik, Kurt and Smola, Alex and Karatzoglou, Alexandros (2004) kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11 (9). pp. 1-20. ISSN 1548-7660

This is the latest version of this item.

[img]
Preview
PDF
Download (427Kb) | Preview

Abstract

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.

Item Type: Article
Keywords: kernel methods / support vector machines / quadratic programming / ranking / clustering / S4 / R
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics > Hornik
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 24 Oct 2013 16:17
Last Modified: 27 Feb 2017 11:36
FIDES Link: https://bach.wu.ac.at/d/research/results/28831/
URI: http://epub.wu.ac.at/id/eprint/3999

Available Versions of this Item

Actions

View Item