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In order to model an efficient learning paradigm, iterative learningalgorithms access data one by one, updating the current hypothesis withoutregress to past data. Past research on iterative learning analyzed for examplemany important additional requirements and their impact on iterative learners.In this paper, our results are twofold. First, we analyze the relative learningpower of various settings of iterative learning, including learning from textand from informant, as well as various further restrictions, for example weshow that strongly non-U-shaped learning is restrictive for iterative learningfrom informant. Second, we investigate the learnability of the concept class ofhalf-spaces and provide a constructive iterative algorithm to learn the set ofhalf-spaces from informant.

Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners