How to determine the size of training data using VC dimension?

I want to determine the size of training data ($m$) when I know the parameters $VC(H)$, $δ$ and $e$. As I know the $VC$ bound satisfy this equation:

$$\mathrm{error}_{\mathrm{true}}(h) \le \mathrm{error}_{\mathrm{train}}(h) + \sqrt\frac{VC(H) \times \ln\left(\frac{2m}{VC(H)} + 1\right) + \ln(4δ)}m$$

but how can I determine the size of training data ($m$) if I know the others?

Suppose you're aiming for a specific error rate. Suppose for the moment that there is no training error. You have an inequality involving all your known parameters and the unknown $m$, and you can solve it to obtain a value of $m$ that guarantees the specific true error rate, assuming that there is no training error. If you then run classification, you can correct for the training error and repeat.
$m \geq \frac{1}{e}(8\times VC(H)\times log_2(\frac{13}{e})+4\times log_2(\frac{2}{\delta}))$