kernlab v0.9-13
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Kernel-based Machine Learning Lab
Kernel-based machine learning methods for classification,
regression, clustering, novelty detection, quantile regression
and dimensionality reduction. Among other methods kernlab
includes Support Vector Machines, Spectral Clustering, Kernel
PCA and a QP solver.
Functions in kernlab
| Name | Description | |
| inlearn | Onlearn object initialization | |
| kha | Kernel Principal Components Analysis | |
| kmmd-class | Class "kqr" | |
| sigest | Hyperparameter estimation for the Gaussian Radial Basis kernel | |
| spam | Spam E-mail Database | |
| ipop-class | Class "ipop" | |
| kfa | Kernel Feature Analysis | |
| as.kernelMatrix | Assing kernelMatrix class to matrix objects | |
| kcca-class | Class "kcca" | |
| kpca-class | Class "kpca" | |
| rvm | Relevance Vector Machine | |
| kqr | Kernel Quantile Regression. | |
| onlearn-class | Class "onlearn" | |
| promotergene | E. coli promoter gene sequences (DNA) | |
| kernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" | |
| spirals | Spirals Dataset | |
| kcca | Kernel Canonical Correlation Analysis | |
| couple | Probabilities Coupling function | |
| plot | plot method for support vector object | |
| kqr-class | Class "kqr" | |
| specc-class | Class "specc" | |
| inchol | Incomplete Cholesky decomposition | |
| gausspr | Gaussian processes for regression and classification | |
| gausspr-class | Class "gausspr" | |
| kernelMatrix | Kernel Matrix functions | |
| csi-class | Class "csi" | |
| ipop | Quadratic Programming Solver | |
| inchol-class | Class "inchol" | |
| kmmd | Kernel Maximum Mean Discrepancy. | |
| ranking | Ranking | |
| kpca | Kernel Principal Components Analysis | |
| income | Income Data | |
| predict.ksvm | predict method for support vector object | |
| ticdata | The Insurance Company Data | |
| kfa-class | Class "kfa" | |
| kha-class | Class "kha" | |
| musk | Musk data set | |
| csi | Cholesky decomposition with Side Information | |
| ksvm-class | Class "ksvm" | |
| onlearn | Kernel Online Learning algorithms | |
| lssvm | Least Squares Support Vector Machine | |
| prc-class | Class "prc" | |
| rvm-class | Class "rvm" | |
| vm-class | Class "vm" | |
| predict.kqr | Predict method for kernel Quantile Regression object | |
| lssvm-class | Class "lssvm" | |
| specc | Spectral Clustering | |
| ranking-class | Class "ranking" | |
| dots | Kernel Functions | |
| ksvm | Support Vector Machines | |
| stringdot | String Kernel Functions | |
| predict.gausspr | predict method for Gaussian Processes object | |
| kkmeans | Kernel k-means | |
| reuters | Reuters Text Data | |
| No Results! | ||
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Details
| Date | 2011-08-08 |
| LazyLoad | Yes |
| License | GPL-2 |
| Packaged | 2011-08-08 06:07:25 UTC; hornik |
| Repository | CRAN |
| Date/Publication | 2011-08-08 06:16:17 |
| depends | base (>= 2.10) , methods , R (>= 2.10) |
| Contributors | Alex Smola, Kurt Hornik, Alexandros Karatzoglou |
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