unclassified data points in libsvm?

Question

SVM tutorials state that if a data point falls in the area surrounding the separating line (in the margin) - it isn't classified. How is this implemented in libraries like SVMlight and libsvm?

Answer 1

For two-class classification, we usually assume that their targets are +1 and -1 respectively. Then we find the maximum-margin hyperplane by a QP solver. Because of soft margin (see the term C in SVM}, some samples exist in the margin.

But that's not problem. We can determine the class that positive values as +-class and negative values as --class

To sum up, Even if the samples are trained as +1 and -1, SVM classifies +-class when >= 0 or --class when < 0