**Author: Thomas Zeugmann**

Source:
Theoretical Computer Science, Vol. 364, Issue 1, 2006, 77-97.
(Special Issue Algorithmic Learning Theory (ALT 2003)).
Since the main focus is put on the efficiency of learning, we also deal with postulates of naturalness and their impact to the efficiency of limit learners. In particular, we look at the learnability of the class of all pattern languages and ask whether or not one can design a learner within the paradigm of learning in the limit that is nevertheless efficient. For achieving this goal, we deal with iterative learning and its interplay with the hypothesis spaces allowed. This interplay has also a severe impact to postulates of naturalness satisfiable by any learner.
Furthermore, since a limit learner is only supposed to converge,
one never knows at any particular learning stage whether or not
the learner did already succeed. The resulting uncertainty may be
prohibitive in many applications. We survey results to resolve this problem
by outlining a new learning model, called Finally, we apply the techniques developed to the problem of learning conjunctive concepts.
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