OptimizeHyperparameters option to tune soft margin SVM classifier

Hello,

I am building an SVM model with soft margin for calssifying images dataset of 900 instants, into two calsses, but I need to tune the hyperparameter 'Boxconstraint', and I am using 'OptimizeHyperparameters' for such purpose, as below code, however, it takes long time to run the optimizer, around 90 seconds, while if i use the 'rbf' model, it takes less time. I wonder what cause this issue. are there any suggestions to avoid or improve this.

Also, If i want to build a hard margin SVM using 'fitcsvm' function, should I set the parameter 'BoxConstraint' to very high value?

  c = cvpartition(data_lables,'k',10);
  
  opts = struct('Optimizer','bayesopt','ShowPlots',false,'CVPartition',c,...
      'AcquisitionFunctionName','expected-improvement-plus');

  svmmod = fitcsvm(data_features,data_lables,'KernelFunction','linear','CacheSize','maximal',...
      'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',opts);

[Don Mathis]

As for the runtime, linear SVM can often be much slower than RBF SVM, depending on the combination of BoxConstraint and KernelScale. The optimization will sometimes explore those combinations. I don't think it's possible to avoid that when doing hyperparameter search.

[Felipe Assunção]

I also see the same problem for using ftcecoc or fitcensemble (which takes much longer).

When I do the optimization, should I consider my entire dataset that I will use in the classification or would it be just a part? I have currently used my entire set.

Beside this, about this optimization model, should I specify cross-validation in this optimization or just in my classifier?