Learning complexity vs communication complexity
Combinatorics, Probability and Computing
On the limitations of embedding methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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We investigate a new notion of embedding of subsets of {-1,1}n in a given normed space, in a way which preserves the structure of the given set as a class of functions on {1, …, n}. This notion is an extension of the margin parameter often used in Nonparametric Statistics. Our main result is that even when considering “small” subsets of {-1, 1}n, the vast majority of such sets do not embed in a better way than the entire cube in any normed space that satisfies a minor structural assumption. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2005