  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 $\ell_p$-norm for any $p \geq 1$. 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 bounds improve upon
or match existing bounds, which are known only for $1 \leq p \leq 2$.
