MAP Hypotheses and Consistent Learners

• a learning algorithm is a consistent learner if it commits zero errors over the training examples
• every consistent learner outputs a MAP hypothesis if 1) we assume a uniform prior probability distribution over H and if 2) we assume a deterministic noise free training data.
• Find-S & Candidate-Elimination output a MAP hypotheses
• Bayesian perspective can be used to characterize learning algorithms even if they do not explicitly manipulate probabilities