kernlab - An S4 Package for Kernel Methods in R

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

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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 14:17
Last Modified: 24 Oct 2019 13:41

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