articleJournal of Statistical SoftwareJan 1, 2004HEDIAMOND OA

kernlab - An S4 Package for Kernel Methods in R

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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 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.

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Authors

4

Topics & keywords

Keywords
  • Cholesky decomposition
  • Computer science
  • Kernel (algebra)
  • Solver
  • Kernel method
  • Dot product
  • Cluster analysis
  • Kernel principal component analysis
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