kernlab - An S4 package for kernel methods in R

Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis

Publication: Working/Discussion PaperWU Working Paper

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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. (author's abstract)
Original languageEnglish
Place of PublicationVienna
PublisherInstitut für Statistik und Mathematik, WU Vienna University of Economics and Business
Publication statusPublished - 2004

Publication series

SeriesResearch Report Series / Department of Statistics and Mathematics
Number9

Bibliographical note

Earlier version

WU Working Paper Series

  • Research Report Series / Department of Statistics and Mathematics

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