deletions | additions       diff --git a/We_trained_a_Support_Vector__.tex b/We_trained_a_Support_Vector__.tex  index fb98bac..fe5632c 100644  --- a/We_trained_a_Support_Vector__.tex  +++ b/We_trained_a_Support_Vector__.tex 
...
We trained a Support Vector Machine (SVM) classifier with a quadratic kernel on the different values of $k$. Figure \ref{fig:scatterSVM} shows a scatter plot of the cluster centroids in a*b* space. The colors tell whether the cluster is infected or healthy. The 'x' symbols represent the points that were misclassified. We chose a quadratic kernel because the scatter plot show that the clusters cannot be easily separated by a linear function.  The Table \ref{tab:svmTable} \ref{tab:svmTableRGB}  shows the performance of the SVM classifier. The values were computed from a 10-fold cross validation, i.e. the SVM classifier was trained and evaluated 10 times with each iteration having 90\% randomly selected data points for training and 10\% randomly selected data points for testing. At $k=2$, the SVM classifier had a low accuracy. Poor segmentation of the images during the clustering step affected the classifier leading to a low accuracy. Increasing $k$ to 3 and 4 significantly improved the results, having up to 82.2\% and 85.8\% accuracy respectively. We tried to see how much the classifier would improve by having more clusters, but even after doubling the value of $k$ the accuracy only increased by 1.3\%. Figure \ref{fig:accuracyxk} plots the accuracy as a function of $k$. It shows that increasing $k$ further would have minimal effect on the accuracy of the classifier.