It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task.
Empirical results support the theoretical findings.
SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning. The aim is to develop very operational performance estimators that are of actual use when applying SVMs. However, it is very expensive and timeconsuming to come by the labeled documents because labeling must be done by human experts. Learning with many relevant features.