Text Categorization with Support Vector Machines: Learning with Many Relevant F Eatures Text Categorization with Support Vector Machines: Learning with Many Relevant F Eatures
This paper explores the use of Support Vector Machines (SVMs) for learning text classiers from examples. It analyzes the particular properties of learning with text data and identi es, why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety of di erent learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning.