I am new to statistical learning. I have a structure $X$ where I showed its hypothesis class $H$ has VC dimension $d$. All I know now is that I can bound the number of examples by $m\geq \frac{1}{\epsilon}ln \frac{d}{\delta}$ and with probability at least $1-\delta$ I will get a hypothesis with error at most $\epsilon$.
My question concerns what is usually the next step(s),with regard to the big picture of learning a structure $X$, after showing its VCD?
I thought about studying other complexity measures for $X$ but wish to hear others suggestions.