kernlab- AnS4Package for Kernel Methods inR

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.

Keywords

Cholesky decompositionComputer scienceKernel (algebra)SolverKernel methodDot productCluster analysisKernel principal component analysisSupport vector machineTheoretical computer scienceMachine learningMathematicsEigenvalues and eigenvectorsProgramming languageDiscrete mathematics

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Publication Info

Year: 2004
Type: article
Volume: 11
Issue: 9
Citations: 1777
Access: Closed

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APA Style

                            
                                
                                    Alexandros Karatzoglou, 
                                
                                    Alex Smola, 
                                
                                    Kurt Hornik
                                
                                et al.
                            
                            (2004). 
                            <b>kernlab</b>- An<i>S4</i>Package for Kernel Methods in<i>R</i>. 
                            Journal of Statistical Software
                            , 11
                            (9)
                            .
                            https://doi.org/10.18637/jss.v011.i09
                        

Identifiers

DOI: 10.18637/jss.v011.i09