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