How do I find slack variables in SVM?/ Distance to the boundary?

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Hi, I used "svmtrain" to train the algorithm: svmStruct=svmtrain(xdata,group); I used "svmclassify" to classify.
My data is not perfectly linearly separable but I still used a linear classifier. In theory, allowances are made for by a slack variable. (Soft margin) I refer to the toolbox help where the theory is. It mentions the slack variable and 2 ways it is computed.
My issue is that, svmStruct does not save the slack variable. Neither can I find it in the function to recall it and save it.
If not, how can I find the distance from each data point to the boundary?
Can anyone help me with this? Thanks

Answers (2)

the cyclist
the cyclist on 8 Feb 2013
It is the input parameter 'boxconstraint' to the svmtrain() command. The default value is 1.

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Ilya on 10 Feb 2013
By definition, a slack variable for observation x with label y (-1 or +1) is max(0,1-y*f), where f is the SVM prediction (soft score ranging from -inf to +inf). svmclassify does not return the scores, so you need to compute the SVM scores yourself. Start with the definition of the SVM model, compute kernel products, multiply by the alpha coefficients and add the bias term. It is easier than it sounds.
Aliza Rubenstein
Aliza Rubenstein on 19 Jan 2017
Thanks for this information. I think it should be Xnew + shift, not Xnew - shift. shift is the negative of the mean.

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