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SVM hard and soft margins in matlab,

1

$begingroup$

I am comparing the performances of several SVM models in matlab using the fitcsvm function,
and I want to double check that I am using the correct syntax
for hard soft amragins and kernel:
the syntax of hard margin should be as follows, in which the hyperparameter of hard margin cost (boxConstraint) should be infinite

%Hard Margin
SVMModel = fitcsvm(x_train,y_train,'BoxConstraint',Inf);

while the soft margin, the boxConstraint (which is the only hyperparameter needed for soft margin)should be tuned and given a suitable value, for example

 %soft Margin
 SVMModel = fitcsvm(x_train,y_train,'BoxConstraint', 7);

or leave it as default (which has the boxconstraint as 1)

SVMModel = fitcsvm(x_train,y_train);

and for the kernel model, say RBF, both boxconstrains and KernelScale (gamma) should be tuned and used

 SVMModel = fitcsvm(x_train,y_train, KernelFunction, 'RBF',...
'BoxConstraint', 7,'KernelScale', '0.3');

assuming the all hyperparameters are tuned, is the previous syntax considered correct to have hard, soft and kernel models in Matlab?

Also, in case of cross-validation of the model, how to get the best hyperparameters while the SVM model is cross-validated? or selecting the hyperparamters step should be before cross-validating the model?

classification svm matlab hyperparameter

share|improve this question

asked Apr 6 at 17:42

gingin

1949

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  stackoverflow.com/q/55544728/5341713
  $endgroup$
  – Esmailian
  Apr 6 at 18:28
  

  
  
  
  

add a comment | 

1

$begingroup$

I am comparing the performances of several SVM models in matlab using the fitcsvm function,
and I want to double check that I am using the correct syntax
for hard soft amragins and kernel:
the syntax of hard margin should be as follows, in which the hyperparameter of hard margin cost (boxConstraint) should be infinite

%Hard Margin
SVMModel = fitcsvm(x_train,y_train,'BoxConstraint',Inf);

while the soft margin, the boxConstraint (which is the only hyperparameter needed for soft margin)should be tuned and given a suitable value, for example

 %soft Margin
 SVMModel = fitcsvm(x_train,y_train,'BoxConstraint', 7);

or leave it as default (which has the boxconstraint as 1)

SVMModel = fitcsvm(x_train,y_train);

and for the kernel model, say RBF, both boxconstrains and KernelScale (gamma) should be tuned and used

 SVMModel = fitcsvm(x_train,y_train, KernelFunction, 'RBF',...
'BoxConstraint', 7,'KernelScale', '0.3');

assuming the all hyperparameters are tuned, is the previous syntax considered correct to have hard, soft and kernel models in Matlab?

Also, in case of cross-validation of the model, how to get the best hyperparameters while the SVM model is cross-validated? or selecting the hyperparamters step should be before cross-validating the model?

classification svm matlab hyperparameter

share|improve this question

asked Apr 6 at 17:42

gingin

1949

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  stackoverflow.com/q/55544728/5341713
  $endgroup$
  – Esmailian
  Apr 6 at 18:28
  

  
  
  
  

add a comment | 

$begingroup$

I am comparing the performances of several SVM models in matlab using the fitcsvm function,
and I want to double check that I am using the correct syntax
for hard soft amragins and kernel:
the syntax of hard margin should be as follows, in which the hyperparameter of hard margin cost (boxConstraint) should be infinite

%Hard Margin
SVMModel = fitcsvm(x_train,y_train,'BoxConstraint',Inf);

while the soft margin, the boxConstraint (which is the only hyperparameter needed for soft margin)should be tuned and given a suitable value, for example

 %soft Margin
 SVMModel = fitcsvm(x_train,y_train,'BoxConstraint', 7);

or leave it as default (which has the boxconstraint as 1)

SVMModel = fitcsvm(x_train,y_train);

and for the kernel model, say RBF, both boxconstrains and KernelScale (gamma) should be tuned and used

 SVMModel = fitcsvm(x_train,y_train, KernelFunction, 'RBF',...
'BoxConstraint', 7,'KernelScale', '0.3');

assuming the all hyperparameters are tuned, is the previous syntax considered correct to have hard, soft and kernel models in Matlab?

Also, in case of cross-validation of the model, how to get the best hyperparameters while the SVM model is cross-validated? or selecting the hyperparamters step should be before cross-validating the model?

classification svm matlab hyperparameter

share|improve this question

asked Apr 6 at 17:42

gingin

1949

$endgroup$

%Hard Margin
SVMModel = fitcsvm(x_train,y_train,'BoxConstraint',Inf);

 %soft Margin
 SVMModel = fitcsvm(x_train,y_train,'BoxConstraint', 7);

SVMModel = fitcsvm(x_train,y_train);

 SVMModel = fitcsvm(x_train,y_train, KernelFunction, 'RBF',...
'BoxConstraint', 7,'KernelScale', '0.3');

share|improve this question

asked Apr 6 at 17:42

gingin

1949

share|improve this question

asked Apr 6 at 17:42

gingin

1949

asked Apr 6 at 17:42

gingin

1949

asked Apr 6 at 17:42

gingin

1949