We consider bandit optimization of a smooth reward function, where the goal is cumulative regret minimization. This problem has been studied for $α$-Hölder continuous (including Lipschitz) functions with $0<α\leq 1$. Our main result is in generalization of the reward function to Hölder space with exponent $α>1$ to bridge the gap between Lipschitz bandits and infinitely-differentiable models such as linear bandits. For Hölder continuous functions, approaches based on random sampling in bins of a discretized domain suffices as optimal. In contrast, we propose a class of two-layer algorithms that deploy misspecified linear/polynomial bandit algorithms in bins. We demonstrate that the proposed algorithm can exploit higher-order smoothness of the function by deriving a regret upper bound of $\tilde{O}(T^\frac{d+α}{d+2α})$ for when $α>1$, which matches existing lower bound. We also study adaptation to unknown function smoothness over a continuous scale of Hölder spaces indexed by $α$, with a bandit model selection approach applied with our proposed two-layer algorithms. We show that it achieves regret rate that matches the existing lower bound for adaptation within the $α\leq 1$ subset.