Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?
Machine LearningPublished 1 January 2002Open access
Edda Leopold, Jörg Kindermann
Citations403
SJR quartileQ1
SJR score1.15
SNIP2.14
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TL;DR
It is shown that in the case of text classification, term-frequency transformations have a larger impact on the performance of SVM than the kernel itself.
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.
Keywords
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