  In this paper, we study the generalization ability of the wide residual
network on $\mathbb{S}^{d-1}$ with the ReLU activation function. We first show
that as the width $m\rightarrow\infty$, the residual network kernel (RNK)
uniformly converges to the residual neural tangent kernel (RNTK). This uniform
convergence further guarantees that the generalization error of the residual
network converges to that of the kernel regression with respect to the RNTK. As
direct corollaries, we then show $i)$ the wide residual network with the early
stopping strategy can achieve the minimax rate provided that the target
regression function falls in the reproducing kernel Hilbert space (RKHS)
associated with the RNTK; $ii)$ the wide residual network can not generalize
well if it is trained till overfitting the data. We finally illustrate some
experiments to reconcile the contradiction between our theoretical result and
the widely observed ``benign overfitting phenomenon''
