In this paper, we present our solution to the PAN workshop challenge of authorship
attribution. In multiple sub-problems, the original authors from given documents have to be
chosen from a fixed training set. The core of our approach lies in traditional character n-gram
analysis in combination with a linear SVM as a classifier. To find optimal values for our
model parameters, we combined predefined parameters determined by a preliminary run on
a training set run with dynamically determined parameters from an ad-hoc grid search …