expected value for the SVM generalization error
In lecture 14 slide 20 there is the following statement:
Expected value(E_out) = < Expected val(#of support vectors/N1)
That is, the exp val for the upper bound for the E_out is limited by the expected value of the #number of support vectors divided by the number of data points minus 1.
I am trying to find some reference to a book or article that presents this upper bound for the E_out for SVM but I am having a really hard time to find anything. Unfortunatly the course book does not cover SVM at all. Any suggestion about articles that discuss this upper bound for SVM more or less clearly?
