We consider a global complexity bound of regularized Newton methods for
the unconstrained convex optimization. The global complexity bound is an
upper bound of the number of iterations required to get an approximate
solution $x$ such that $f(x)- \inf f(y)\leq \varepsilon$, where $\
varepsilon$ is a given positive constant. Recently, Ueda and
Yamashita proposed the regularized Newton method whose global complexity
bound is $O(\varepsilon^{-2/3})$.
In this paper, we propose an accelerated version of the regularized
Newton method and show that its global complexity bound is $O(\
varepsilon^{-7/3})$.