  In order to model an efficient learning paradigm, iterative learning
algorithms access data one by one, updating the current hypothesis without
regress to past data. Past research on iterative learning analyzed for example
many important additional requirements and their impact on iterative learners.
In this paper, our results are twofold. First, we analyze the relative learning
power of various settings of iterative learning, including learning from text
and from informant, as well as various further restrictions, for example we
show that strongly non-U-shaped learning is restrictive for iterative learning
from informant. Second, we investigate the learnability of the concept class of
half-spaces and provide a constructive iterative algorithm to learn the set of
half-spaces from informant.
