diff --git a/text/thesis/03Results.tex b/text/thesis/03Results.tex
index 8ffc3d4..70f1585 100644
--- a/text/thesis/03Results.tex
+++ b/text/thesis/03Results.tex
@@ -198,13 +198,25 @@
 \section{Cross-validation Parameters}
     \subsection{SVM}
     \label{res:maxC}
-        With a cross validation the results for the soft-margin parameter are compared for $\lambda=0.1,1,10$. The results are shown in figure~\ref{fig:svmCV}.\\
-
-        \begin{figure}
-            \caption{results of cross-validation of the Support Vector Machine}
+        With a cross validation the results for the soft-margin parameter are compared for $\lambda=0.1,1,10$. The results are shown in table~\ref{fig:svmCV}.\\
+        There are no clear preferences for EEG and LF, EMG however seems to prefer a lower parameter $c$. Since this is no clear preference the parameter $c$ of the SVM does not seem to have a great influence.
+        \begin{table}
+            \centering
+            \begin{math}
+                \begin{array}
+                    {r||c|c|c}
+                    c&0.1 & 1 & 10\\\hline
+                    EEG & 158 & 165 & 187\\
+                    EMG &200 &147 & 163\\
+                    LF& 166 & 174 & 170\\
+                    EEGmovements &168 & 159 & 183 \\
+                    EMGmovements &213 & 164 & 133\\
+                    LFmovements& 150 & 187 & 173
+                \end{array}
+            \end{math}
+            \caption{Number of sessions in which the according $c$ was chosen as best parameter when computing an SVM for EEG, EMG or LF data; either for classification into move/rest or into 5 classes}
             \label{fig:svmCV}
-        \end{figure}
-        TODO%TODO
+        \end{table}
     \subsection{RIDGE-Regression}
         In tables \ref{tab:ridgeParamEMGkin}, \ref{tab:ridgeParamHighKin} and \ref{tab:ridgeParamAO6Kin} we find the number of 'wins' for each parameter\footnote{\ref{tab:ridgeParamHighKin} and \ref{tab:ridgeParamAO6Kin} were calculated with a order for Burg's method of 50 instead of the later default of 250}. A 'win' refers to a run where this $\lambda$ scored the highest correlation.\\
         For EMG there is no clear preference but it seems like 100 should work as parameter. For EEG we see a clear preference for $\lambda=100$. Low Frequencies seem to prefer a lower parameter of about 10 however this was only evaluated for one session.