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\begin{appendices}
\section{Proofs for sone Lemmata in ...}
In the following there will be proofs for some important Lemmata in
Section~\ref{sec:theo38}. Further proofs not discussed here can be
found in \textcite{heiss2019}
\begin{Theorem}[Proof of Lemma~\ref{theo38}]
\end{Theorem}
\begin{Lemma}[$\frac{w^{*,\tilde{\lambda}}_k}{v_k}\approx\mathcal{O}(\frac{1}{n})$]
For any $\lambda > 0$ and training data $(x_i^{\text{train}},
y_i^{\text{train}}) \in \mathbb{R}^2, \, i \in
\left\{1,\dots,N\right\}$, we have
\[
\max_{k \in \left\{1,\dots,n\right\}} \frac{w^{*,
\tilde{\lambda}}_k}{v_k} = \po_{n\to\infty}
\]
\end{Lemma}
\end{appendices}
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