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Abstract: Next to prediction accuracy, the interpretability of models is one of the fundamental criteria for machine learning algorithms. While high accuracy learners have intensively been explored, interpretability still poses a di#cult problem, largely because it can hardly be formalized in a general way. To circumvent this problem, one can often find a model in a hypothesis space that the user regards as understandable or minimize a user-defined measure of complexity, such that the obtained... (Update)
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BibTeX entry: (Update)
@misc{ university-learning,
author = "Stefan Ruping University",
title = "Learning with Local Models",
url = "citeseer.ist.psu.edu/757604.html" }
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