trainSVMModel(problem,
svmType = C_SVC,
kernelType = RBF,
degree = 3,
gamma = 0.5,
coefficient0 = 0,
nu = 0.5,
cacheSize = 40,
C = 1,
EPS = 0.001,
P = 0.1,
shrinking = TRUE,
probability = FALSE,
weights = NULL,
verbose = FALSE);
problem
-
svmType
Type of SVM (default C-SVC). [as SvmType]
kernelType
Type of kernel function (default Polynomial). [as KernelType]
degree
Degree in kernel function (default 3). [as integer]
gamma
Gamma in kernel function (default 1/k). [as double]
coefficient0
Zeroeth coefficient in kernel function (default 0). [as double]
nu
The parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5). [as double]
cacheSize
Cache memory size in MB (default 100). [as integer]
C
The parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1). [as double]
EPS
Tolerance of termination criterion (default 0.001). [as double]
P
The epsilon in loss function of epsilon-SVR (default 0.1). [as double]
shrinking
Whether to use the shrinking heuristics, (default True). [as boolean]
probability
Whether to train an SVC or SVR model for probability estimates, (default False). [as boolean]
this function returns data object in these one of the listed data types:
.