kernlab - An S4 package for kernel methods in R

• Main Authors: Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim
• Format: Others
• Language: en
• Published: Institut für Statistik und Mathematik, WU Vienna University of Economics and Business 2004
• Subjects: kernel methods / support vector machines / quadratic programming / ranking / clustering / S4 / R
• Online Access: http://epub.wu.ac.at/1048/1/document.pdf

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) === Series: Research Report Series / Department of Statistics and Mathematics