kernlab - An S4 Package for Kernel Methods in R
Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis
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.
Citation edge evidence
- kernlab - An S4 Package for Kernel Methods in R→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Statistical Learning TheoryLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Advances in kernel methods: support vector learningLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho…Legacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Making Large-Scale SVM Learning PracticalLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Learning with KernelsLegacy edge — source detail unavailable
- kernlab - An S4 Package for Kernel Methods in R→Support vector domain descriptionLegacy edge — source detail unavailable
- Support vector domain description→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Making Large-Scale SVM Learning PracticalLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Advances in kernel methods: support vector learningLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→Statistical Learning TheoryLegacy edge — source detail unavailable
- Estimating the Support of a High-Dimensional Distribution→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Statistical Learning Theory→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
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Topics
| Gaussian Processes and Bayesian Inference | Computer Science |
| Neural Networks and Applications | Computer Science |
| Time Series Analysis and Forecasting | Computer Science |
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