The analysis of theoretical learning models is basically concerned with the comparison of identification capabilities in different models. Modifications of the formal constraints affect the quality of the corresponding learners on the one hand and regulate the quantity of learnable classes on the other hand. For many inductive inference models-such as Gold's identification in the limit-the corresponding relationships of learning potential provided by the compatible learners are well-known. Recent work even corroborates the relevance of these relationships by revealing them still in the context of uniform Gold-style learning. Uniform learning is rather concerned with the synthesis of successful learners instead of their mere existence. The subsequent analysis further strengthens the results regarding uniform learning, particularly aiming at the design of methods for increasing the potential of the relevant learners. This demonstrates how to improve given learning strategies instead of just verifying the existence of more powerful uniform learners. For technical reasons these results are achieved using various formal conditions concerning the learnability of unions of uniformly learnable classes. Therefore numerous sufficient properties for the learnability of such unions are presented and illustrated with several examples.
['Sandra Zilles']
Increasing the power of uniform inductive learners