Reputation: 2837
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?
Upvotes: 1
Views: 89
Reputation: 6186
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
Upvotes: 1