## Language Learning in Dependence on the Space of Hypotheses
In particular, it is proved that, whenever monotonicity requirements are involved, then exact learning is almost always weaker than class preserving inference which itself turns out to be almost always weaker than class comprising learning. Next, we provide additionally insight into the problem under what conditions, for example, exact and class preserving learning procedures are of equal power. Finally, we deal with the question what kind of languages has to be added to the space of hypotheses in order to obtain superior learning algorithms.
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