R: Kernel-Based Machine Learning Lab

# Kernel-Based Machine Learning Lab

## Documentation for package ‘kernlab’ version 0.9-33

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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"