Quote:
Originally Posted by yaser
Try 10,000 points for testing. Since you say that the error is zero no matter how many times you generated new 100 points, you may also want to check the seeding of the random number generator to make sure that each 100 points are really different from the previous set.
A zero outofsample error means that the target boundary was perfectly replicated, an illogical event when we train with only 100 points.

I think i still have problems with kernel vector machine, as I calculated alpha from quadratic programming , which is (N*1) matrix, Then I calculated b by using
MATLAB CODE %%%%
[maxval, maxind] = max(alpha);
b=1/y(maxind) kernel_x(maxind,
*(alpha.*y);
%%%%
and i was unable to get w , then Ein was measured by
%%%
pred=sign(kernel_x*alpha.*y+b);
Ein_svm=numel(find(pred~=y));
For test , i used
pred=sign(test_kernel_x*alpha.*y+b);
Eout_svm=numel(find(pred~=y_test)).
as if the number of test data points are not consistent with number of training data points. test_kernel_x*alpha.*y is unable to be calculated , as dimensions do not match.......
I think there is some confusion about my understanding to kernel vector machine.
isn't w unachievable? Thanks professor.