found in generalization or some complexity over N for example in kernel methods it would be Y transpose K inverse Y that's sort of the RK just norm in Ridge regression it could be VC dimension VC dimensions roughly number of parameters times the depth in feed-forward networks the required list all kind of big they're bigger the sample size which is exactly what the I Claire paper was pointing out it was pointing L essentially that the number of parameters is roughly 30 20 30 times the number of samples in many