Kernel-Based Machine Learning Lab
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Documentation for package ‘kernlab’ version 0.9-32
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A B C D E F G H I K L M N O P Q R S T V W X Y
-- A --
| alpha | Class "vm" |
| alpha-method | Class "gausspr" |
| alpha-method | Class "kfa" |
| alpha-method | Class "kqr" |
| alpha-method | Class "ksvm" |
| alpha-method | Class "lssvm" |
| alpha-method | Class "onlearn" |
| alpha-method | Class "rvm" |
| alpha-method | Class "vm" |
| alphaindex | Class "ksvm" |
| alphaindex-method | Class "gausspr" |
| alphaindex-method | Class "kfa" |
| alphaindex-method | Class "kqr" |
| alphaindex-method | Class "ksvm" |
| alphaindex-method | Class "lssvm" |
| anovadot | Kernel Functions |
| anovakernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| as.kernelMatrix | Assing kernelMatrix class to matrix objects |
| as.kernelMatrix-method | Assing kernelMatrix class to matrix objects |
| as.kernelMatrix-methods | Assing kernelMatrix class to matrix objects |
| Asymbound | Kernel Maximum Mean Discrepancy. |
| Asymbound-method | Class "kqr" |
| AsympH0 | Kernel Maximum Mean Discrepancy. |
| AsympH0-method | Class "kqr" |
-- B --
| b | Class "ksvm" |
| b-method | Class "kqr" |
| b-method | Class "ksvm" |
| b-method | Class "lssvm" |
| b-method | Class "onlearn" |
| besseldot | Kernel Functions |
| besselkernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| buffer | Class "onlearn" |
| buffer-method | Class "onlearn" |
-- C --
| centers | Class "specc" |
| centers-method | Class "specc" |
| coef-method | Gaussian processes for regression and classification |
| coef-method | Kernel Feature Analysis |
| coef-method | Kernel Quantile Regression. |
| coef-method | Class "ksvm" |
| coef-method | Support Vector Machines |
| coef-method | Least Squares Support Vector Machine |
| coef-method | Relevance Vector Machine |
| convergence | Class "ranking" |
| convergence-method | Class "ranking" |
| couple | Probabilities Coupling function |
| cross | Class "vm" |
| cross-method | Class "gausspr" |
| cross-method | Class "kqr" |
| cross-method | Class "ksvm" |
| cross-method | Class "lssvm" |
| cross-method | Class "rvm" |
| cross-method | Class "vm" |
| csi | Cholesky decomposition with Side Information |
| csi-class | Class "csi" |
| csi-method | Cholesky decomposition with Side Information |
| csi-methods | Cholesky decomposition with Side Information |
-- D --
| diagresidues | Class "inchol" |
| diagresidues-method | Class "csi" |
| diagresidues-method | Class "inchol" |
| dots | Kernel Functions |
| dual | Class "ipop" |
| dual-method | Class "ipop" |
-- E --
| edgegraph | Class "ranking" |
| edgegraph-method | Class "ranking" |
| eig | Class "prc" |
| eig-method | Class "kha" |
| eig-method | Class "kpca" |
| eig-method | Class "prc" |
| error | Class "vm" |
| error-method | Class "gausspr" |
| error-method | Class "kqr" |
| error-method | Class "ksvm" |
| error-method | Class "lssvm" |
| error-method | Class "rvm" |
| error-method | Class "vm" |
| eskm-method | Class "kha" |
-- F --
| fit-method | Class "onlearn" |
| fitted-method | Class "ksvm" |
| fitted-method | Class "vm" |
| fourierdot | Kernel Functions |
| fourierkernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
-- G --
| gausspr | Gaussian processes for regression and classification |
| gausspr-class | Class "gausspr" |
| gausspr-method | Gaussian processes for regression and classification |
-- H --
| H0 | Kernel Maximum Mean Discrepancy. |
| H0-method | Class "kqr" |
| how | Class "ipop" |
| how-method | Class "ipop" |
-- I --
| inchol | Incomplete Cholesky decomposition |
| inchol-class | Class "inchol" |
| inchol-method | Incomplete Cholesky decomposition |
| income | Income Data |
| inlearn | Onlearn object initialization |
| inlearn-method | Onlearn object initialization |
| ipop | Quadratic Programming Solver |
| ipop-class | Class "ipop" |
| ipop-method | Quadratic Programming Solver |
-- K --
| kcall | Class "vm" |
| kcall-method | Class "gausspr" |
| kcall-method | Class "kfa" |
| kcall-method | Class "kha" |
| kcall-method | Class "kpca" |
| kcall-method | Class "kqr" |
| kcall-method | Class "ksvm" |
| kcall-method | Class "lssvm" |
| kcall-method | Class "prc" |
| kcall-method | Class "rvm" |
| kcall-method | Class "vm" |
| kcca | Kernel Canonical Correlation Analysis |
| kcca-class | Class "kcca" |
| kcca-method | Kernel Canonical Correlation Analysis |
| kcor | Class "kcca" |
| kcor-method | Class "kcca" |
| kernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| kernelf | Class "vm" |
| kernelf-method | Class "gausspr" |
| kernelf-method | Class "kfa" |
| kernelf-method | Class "kha" |
| kernelf-method | Class "kqr" |
| kernelf-method | Class "kpca" |
| kernelf-method | Class "kqr" |
| kernelf-method | Class "ksvm" |
| kernelf-method | Class "lssvm" |
| kernelf-method | Class "onlearn" |
| kernelf-method | Class "prc" |
| kernelf-method | Class "rvm" |
| kernelf-method | Class "specc" |
| kernelf-method | Class "vm" |
| kernelFast | Kernel Matrix functions |
| kernelFast-method | Kernel Matrix functions |
| kernelMatrix | Kernel Matrix functions |
| kernelMatrix-class | Assing kernelMatrix class to matrix objects |
| kernelMatrix-method | Kernel Matrix functions |
| kernelMult | Kernel Matrix functions |
| kernelMult-method | Kernel Matrix functions |
| kernelPol | Kernel Matrix functions |
| kernelPol-method | Kernel Matrix functions |
| kernels | Kernel Functions |
| kfa | Kernel Feature Analysis |
| kfa-class | Class "kfa" |
| kfa-method | Kernel Feature Analysis |
| kfunction | Kernel Functions |
| kfunction-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| kha | Kernel Principal Components Analysis |
| kha-class | Class "kha" |
| kha-method | Kernel Principal Components Analysis |
| kkmeans | Kernel k-means |
| kkmeans-method | Kernel k-means |
| kmmd | Kernel Maximum Mean Discrepancy. |
| kmmd-class | Class "kqr" |
| kmmd-method | Kernel Maximum Mean Discrepancy. |
| kpar | Kernel Functions |
| kpar-method | Class "gausspr" |
| kpar-method | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| kpar-method | Class "kqr" |
| kpar-method | Class "ksvm" |
| kpar-method | Class "lssvm" |
| kpar-method | Class "onlearn" |
| kpar-method | Class "rvm" |
| kpar-method | Class "vm" |
| kpca | Kernel Principal Components Analysis |
| kpca-class | Class "kpca" |
| kpca-method | Kernel Principal Components Analysis |
| kqr | Kernel Quantile Regression. |
| kqr-class | Class "kqr" |
| kqr-method | Kernel Quantile Regression. |
| ksvm | Support Vector Machines |
| ksvm-class | Class "ksvm" |
| ksvm-method | Support Vector Machines |
-- L --
| laplacedot | Kernel Functions |
| laplacekernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| lev | Class "vm" |
| lev-method | Class "gausspr" |
| lev-method | Class "ksvm" |
| lev-method | Class "lssvm" |
| lev-method | Class "rvm" |
| lev-method | Class "vm" |
| lssvm | Least Squares Support Vector Machine |
| lssvm-class | Class "lssvm" |
| lssvm-method | Least Squares Support Vector Machine |
| lssvm-methods | Least Squares Support Vector Machine |
-- M --
| maxresiduals | Class "inchol" |
| maxresiduals-method | Class "csi" |
| maxresiduals-method | Class "inchol" |
| mlike | Class "rvm" |
| mlike-method | Class "rvm" |
| mmdstats | Kernel Maximum Mean Discrepancy. |
| mmdstats-method | Class "kqr" |
| musk | Musk data set |
-- N --
| nSV | Class "ksvm" |
| nSV-method | Class "ksvm" |
| nSV-method | Class "lssvm" |
| nvar | Class "rvm" |
| nvar-method | Class "rvm" |
-- O --
| obj | Class "ksvm" |
| obj-method | Class "ksvm" |
| onlearn | Kernel Online Learning algorithms |
| onlearn-class | Class "onlearn" |
| onlearn-method | Kernel Online Learning algorithms |
-- P --
| param | Class "ksvm" |
| param-method | Class "kqr" |
| param-method | Class "ksvm" |
| param-method | Class "lssvm" |
| pcv | Class "prc" |
| pcv-method | Class "kha" |
| pcv-method | Class "kpca" |
| pcv-method | Class "prc" |
| pivots | Class "inchol" |
| pivots-method | Class "csi" |
| pivots-method | Class "inchol" |
| plot-method | plot method for support vector object |
| plot.ksvm | plot method for support vector object |
| polydot | Kernel Functions |
| polykernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| prc-class | Class "prc" |
| predgain | Class "csi" |
| predgain-method | Class "csi" |
| predict-method | Class "kfa" |
| predict-method | Kernel Principal Components Analysis |
| predict-method | Kernel Principal Components Analysis |
| predict-method | Least Squares Support Vector Machine |
| predict-method | Class "onlearn" |
| predict-method | predict method for Gaussian Processes object |
| predict-method | Predict method for kernel Quantile Regression object |
| predict-method | predict method for support vector object |
| predict-method | Relevance Vector Machine |
| predict.gausspr | predict method for Gaussian Processes object |
| predict.kqr | Predict method for kernel Quantile Regression object |
| predict.ksvm | predict method for support vector object |
| primal | Class "ipop" |
| primal-method | Class "ipop" |
| prior | Class "ksvm" |
| prior-method | Class "ksvm" |
| prob.model | Class "ksvm" |
| prob.model-method | Class "ksvm" |
| promotergene | E. coli promoter gene sequences (DNA) |
-- Q --
| Q | Class "csi" |
| Q-method | Class "csi" |
-- R --
| R | Class "csi" |
| R-method | Class "csi" |
| Radbound | Kernel Maximum Mean Discrepancy. |
| Radbound-method | Class "kqr" |
| ranking | Ranking |
| ranking-class | Class "ranking" |
| ranking-method | Ranking |
| rbfdot | Kernel Functions |
| rbfkernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| reuters | Reuters Text Data |
| rho | Class "onlearn" |
| rho-method | Class "onlearn" |
| rlabels | Reuters Text Data |
| rotated | Class "kpca" |
| rotated-method | Class "kpca" |
| RVindex | Class "rvm" |
| RVindex-method | Class "rvm" |
| rvm | Relevance Vector Machine |
| rvm-class | Class "rvm" |
| rvm-method | Relevance Vector Machine |
| rvm-methods | Relevance Vector Machine |
-- S --
| scaling | Class "ksvm" |
| scaling-method | Class "gausspr" |
| scaling-method | Class "kqr" |
| scaling-method | Class "ksvm" |
| scaling-method | Class "lssvm" |
| show | Class "ksvm" |
| show-method | Kernel Functions |
| show-method | Gaussian processes for regression and classification |
| show-method | Kernel Feature Analysis |
| show-method | Kernel Maximum Mean Discrepancy. |
| show-method | Kernel Quantile Regression. |
| show-method | Support Vector Machines |
| show-method | Least Squares Support Vector Machine |
| show-method | Class "onlearn" |
| show-method | Class "ranking" |
| show-method | Relevance Vector Machine |
| show-method | Spectral Clustering |
| sigest | Hyperparameter estimation for the Gaussian Radial Basis kernel |
| sigest-method | Hyperparameter estimation for the Gaussian Radial Basis kernel |
| size | Class "specc" |
| size-method | Class "specc" |
| spam | Spam E-mail Database |
| specc | Spectral Clustering |
| specc-class | Class "specc" |
| specc-method | Spectral Clustering |
| spirals | Spirals Dataset |
| splinedot | Kernel Functions |
| splinekernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| stringdot | String Kernel Functions |
| stringkernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| SVindex | Class "ksvm" |
| SVindex-method | Class "ksvm" |
-- T --
| tanhdot | Kernel Functions |
| tanhkernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| ticdata | The Insurance Company Data |
| truegain | Class "csi" |
| truegain-method | Class "csi" |
| type | Class "vm" |
| type-method | Class "gausspr" |
| type-method | Class "ksvm" |
| type-method | Class "lssvm" |
| type-method | Class "onlearn" |
| type-method | Class "rvm" |
| type-method | Class "vm" |
-- V --
| vanilladot | Kernel Functions |
| vanillakernel-class | Class "kernel" "rbfkernel" "polykernel", "tanhkernel", "vanillakernel" |
| vm-class | Class "vm" |
-- W --
| withinss | Class "specc" |
| withinss-method | Class "specc" |
-- X --
| xcoef | Class "kcca" |
| xcoef-method | Class "kcca" |
| xmatrix | Class "vm" |
| xmatrix-method | Class "gausspr" |
| xmatrix-method | Class "kfa" |
| xmatrix-method | Class "kha" |
| xmatrix-method | Class "kpca" |
| xmatrix-method | Class "kqr" |
| xmatrix-method | Class "ksvm" |
| xmatrix-method | Class "lssvm" |
| xmatrix-method | Class "onlearn" |
| xmatrix-method | Class "prc" |
| xmatrix-method | Class "rvm" |
| xmatrix-method | Class "vm" |
| xvar-method | Class "kcca" |
-- Y --
| ycoef | Class "kcca" |
| ycoef-method | Class "kcca" |
| ymatrix | Class "vm" |
| ymatrix-method | Class "gausspr" |
| ymatrix-method | Class "kqr" |
| ymatrix-method | Class "ksvm" |
| ymatrix-method | Class "lssvm" |
| ymatrix-method | Class "rvm" |
| ymatrix-method | Class "vm" |
| yvar-method | Class "kcca" |