













• 
We want to get a
low error rate on unseen data.




– 
This
is called “structural risk minimization”



• 
It would be
really helpful if we could get a guarantee of


the following
form:




Test
error rate =< train error rate + f(N, h, p)




Where
N = size of training set,




h = measure of the model
complexity,




p = the probability that this
bound fails




We
need p to allow for really unlucky test sets.



• 
Then we could
choose the model complexity that



minimizes the
bound on the test error rate.

