We present a method for learning classification functions from pre-classified training examples and hypotheses written roughly by experts. The goal is to produce a classification function that has higher accuracy than either the expert's hypotheses or the classification function inductively learned from the training examples alone. The key idea in our proposed approach is to let the expert's hypotheses influence the process of learning inductively from the training examples. Experimental results are presented demonstrating the power of our approach in a variety of domains.