| lssvm-class {kernlab} | R Documentation |
The Gaussian Processes object
Objects can be created by calls of the form new("lssvm", ...).
or by calling the lssvm function
tol:"numeric" contains
tolerance of termination criteriakernelf:"kfunction" contains
the kernel function usedkpar:"list" contains the
kernel parameter used param:"list" contains the
regularization parameter used.kcall:"call" contains the used
function call type:"character" contains
type of problem terms:"ANY" contains the
terms representation of the symbolic model used (when using a formula)xmatrix:"matrix" containing
the data matrix used ymatrix:"output" containing the
response matrixfitted:"output" containing the
fitted values lev:"vector" containing the
levels of the response (in case of classification) scaling:"ANY" containing the
scaling information performed on the datanclass:"numeric" containing
the number of classes (in case of classification) alpha:"listI" containing the
computes alpha values alphaindex"list" containing
the indexes for the alphas in various classes (in multi-class problems).nvar:"numeric" containing the
computed varianceerror:"numeric" containing the
training errorcross:"numeric" containing the
cross validation errorn.action:"ANY" containing the
action performed in NA signature(object = "lssvm"): returns the alpha
vectorsignature(object = "lssvm"): returns the cross
validation error signature(object = "lssvm"): returns the
training error signature(object = "vm"): returns the fitted values signature(object = "lssvm"): returns the call performedsignature(object = "lssvm"): returns the
kernel function usedsignature(object = "lssvm"): returns the kernel
parameter usedsignature(object = "lssvm"): returns the regularization
parameter usedsignature(object = "lssvm"): returns the
response levels (in classification) signature(object = "lssvm"): returns the type
of problemsignature(object = "ksvm"): returns the
scaling values signature(object = "lssvm"): returns the
data matrix usedsignature(object = "lssvm"): returns the
response matrix used
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
# train model data(iris) test <- lssvm(Species~.,data=iris,var=2) test alpha(test) error(test) lev(test)