Text Categorization with Support Vector Machines: Learning with Many Relevant F Eatures Text Categorization with Support Vector Machines: Learning with Many Relevant F Eatures
نویسنده
چکیده
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.
منابع مشابه
Universit at Dortmund Fachbereich Informatik Lehrstuhl Viii K Unstliche Intelligenz Text Categorization with Support Vector Machines: Learning with Many Relevant Features Text Categorization with Support Vector Machines: Learning with Many Relevant Features
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 o...
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This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs arc appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of...
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