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On the Rademacher Complexity of Linear Hypothesis Sets
[article]
2020
arXiv
pre-print
Preserved Fulltext
Linear predictors form a rich class of hypotheses used in a variety of learning algorithms. We present a tight analysis of the empirical Rademacher complexity of the family of linear hypothesis classes with weight vectors bounded in ℓ_p-norm for any p ≥ 1. This provides a tight analysis of generalization using these hypothesis sets and helps derive sharp data-dependent learning guarantees. We give both upper and lower bounds on the Rademacher complexity of these families and show that our
arXiv:2007.11045v1
fatcat:cbekymbumvddrcmlsx5zn4kvzq