Hi Konstantinos,
The adjustments you would make would highly depend on your specific data, but to clarify how the BoxConstraint impacts your SVM, this parameter lets you specify the soft margin in the SVM classifier you train. This in turn lets you control the trade-off between margin width and misclassification by specifying a single (scalar) value that specifies the soft margin for all vectors. To specify a hard margin, you can set this value to a very large number. Theoretically it could be set to infinity (inf in MATLAB), but this may cause numerical stability issues in the optimization algorithm and increases the training time (although it will also decrease the number of support vectors). In addition, I would recommend using "fitcsvm" instead of "svmtrain", as svmtrain will be removed in a future release of MATLAB.
Hope this helps!
