A way to choose a model class
	• 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.