| Summary: | 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.
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