Text categorization with support vector machines. How to represent texts in input space ?

The choice of the kernel function is crucial to most applications of suppor t vector machines. In this paper, however, we show that in the case of text classification, term-frequency transformations have a larger impact on the performance of SVM than the kernel itself. We discuss the role of importan ce-weights (e.g. document frequency and redundancy), which is not yet fully understood in the light of model complexity and calculation cost, and we s how that time consuming lemmatization or stemming can be avoided even when classifying a highly inflectional language like German.