Computational learning theory studies other models (other than PAC) were
the order of the training examples is varied,
there is noise in the data,
the definition of success is different,
the learner makes different assumptions about the distribution of instances, etc.
We will now look at the mistake bound model of learning in which the learner is evaluated by the total number of mistakes it makes before it converges to the correct hypothesis.