SVM find member of outside training set
The classic SVM does part the n-dimensional feature space with planes. That means every point in space is in one of the partitions and therefore belongs to one of the trained classes. there is no outlier detection.
However there is also the concept of a one-class SVM that tries to encapsulate the "known" space and classifies into "known" and "unknown". The libSVM package also has probabilities, you could try to analyse if that helps. You could also try other classification concepts to  detect outliers like nearest neighbour
I try to make classification multiclass with SVM on OpenCV (I use openCV for python). Let's say if I have 5 class and training it well. I have been test it, and got good result.
The problem appear when object from 6th class come to this classification. Althought I haven't train this class before, why I got result this object (that come from 6th class) recognize as object from one of the class that I have been train before (It classified as member of 1st,or 2nd, ect class). While machine should say didn't know it from what class. SVM at OpenCV didn't return the probability, it just return the label of the class.
I have an idea to make it 2 times classification. First with biner classification, with all of the sample as the training set. Second I classify it to multiclass.
But the problem, how should I find the negative sample for the first classification while I didn't know the other object (let's say that come from 6th or 7th class). Anybody can help me, what should I do? Which samples that should I use as Negative Sample? It is the great idea or stupid idea? Is there other way to solve this problem?