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Fat-Shattering Dimension of -fold Aggregations
Journal of machine learning research (JMLR), 2021
Abstract
We provide estimates on the fat-shattering dimension of aggregation rules of real-valued function classes. The latter consists of all ways of choosing functions, one from each of the classes, and computing a pointwise function of them, such as the median, mean, and maximum. The bound is stated in terms of the fat-shattering dimensions of the component classes. For linear and affine function classes, we provide a considerably sharper upper bound and a matching lower bound, achieving, in particular, an optimal dependence on . Along the way, we improve several known results in addition to pointing out and correcting a number of erroneous claims in the literature.
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