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Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. Fordoi:10.1109/sfcs.1987.37 dblp:conf/focs/Littlestone87 fatcat:l5jws7bmqbdwnepdh7rwhcxs44