Parameter {Microsoft.VisualBasic.MachineLearning.SVM}

.NET clr documentation

Parameter

Description

This class contains the various parameters which can affect the way in which an SVM is learned. Unless you know what you are doing, chances are you are best off using the default values.

Declare

            
# namespace Microsoft.VisualBasic.MachineLearning.SVM
export class Parameter {
   # The parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
   c: double;
   # Cache memory size in MB (default 100)
   cacheSize: double;
   # Zeroeth coefficient in kernel function (default 0)
   coefficient0: double;
   # Degree in kernel function (default 3).
   degree: integer;
   # Tolerance of termination criterion (default 0.001)
   EPS: double;
   # Gamma in kernel function (default 1/k)
   gamma: double;
   # Type of kernel function (default Polynomial)
   kernelType: KernelType;
   # The parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
   nu: double;
   # The epsilon in loss function of epsilon-SVR (default 0.1)
   P: double;
   # Whether to train an SVC or SVR model for probability estimates, (default False)
   probability: boolean;
   # Whether to use the shrinking heuristics, (default True)
   shrinking: boolean;
   # Type of SVM (default C-SVC)
   svmType: SvmType;
   # ColorClass.name
   weights: list;
}

        

.NET clr type reference tree

1. use by property member kernelType: KernelType
2. use by property member svmType: SvmType
3. use by property member weights: list

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