Abstract. The choice of the kernel function is crucial to most applications of support 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 importance-weights (e.g. document frequency and redundancy), which is not yet fully understood in the light of model complexity and calculation cost, and we show that time consuming lemmatization or stemming can be avoided even when classifying a highly inflectional language like German.
However, Leopold et al. REF show that, in the case of text classification, term-frequency transformations have a larger impact on the performance of the SVM than the kernel itself.
Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?
Machine Learning
Machine Learning
Computer Science