Gaussian Process with Radial Basis Function Kernel

method = 'gaussprRadial'

Type: Regression, Classification

Tuning Parameters: sigma (Sigma)

Least Squares Support Vector Machine with Radial Basis Function Kernel

method = 'lssvmRadial'

Type: Classification

Tuning Parameters: sigma (Sigma)

Radial Basis Function Kernel Regularized Least Squares

method = 'krlsRadial'

Type: Regression

Tuning Parameters: lambda (Regularization Parameter), sigma (Sigma)

Radial Basis Function Network

method = 'rbf'

Type: Classification

Tuning Parameters: size (#Hidden Units)

Radial Basis Function Network

method = 'rbfDDA'

Type: Regression, Classification

Tuning Parameters: negativeThreshold (Activation Limit for Conflicting Classes)

Relevance Vector Machines with Radial Basis Function Kernel

method = 'rvmRadial'

Type: Regression

Tuning Parameters: sigma (Sigma)

Support Vector Machines with Class Weights

method = 'svmRadialWeights'

Type: Classification

Tuning Parameters: sigma (Sigma), C (Cost), Weight (Weight)

Support Vector Machines with Radial Basis Function Kernel

method = 'svmRadial'

Type: Regression, Classification

Tuning Parameters: sigma (Sigma), C (Cost)

Support Vector Machines with Radial Basis Function Kernel

method = 'svmRadialCost'

Type: Regression, Classification

Tuning Parameters: C (Cost)

Variational Bayesian Multinomial Probit Regression

method = 'vbmpRadial'

Type: Classification

Tuning Parameters: estimateTheta (Theta Estimated)