| Summary: | 碩士 === 義守大學 === 資訊工程學系 === 90 === ABSTRACT
In this thesis, we propose a new method of text classification based on multiclass support vector learning. Support Vector Machines (SVMs) are learning systems that use a hypothesis space of linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. With the rapid growth of online information, text classification has become one of the key techniques for handling and organizing text data. Text classification is used to classify news stories and to find interesting information on the World Wide Web. To find out what methods are promising for learning text classifiers, we should find out more about the properties of text: (1) High dimensional input space; (2) Few irrelevant features; (3) Document vectors are sparse; (4) Most text categorization problems are linearly separable. In multiclass support vector learning, we propose an improved Decision Directed Acyclic Graph classifier strategy with application in text classification. We develop software tools for rapid and accurate text classification. This provides an alternative approach to undertake the highly complex problems of database search and organization. Benchmark datasets with different characteristics are used for comparative study.
Keywords: Support Vector Machines, Text Classification, Multiclass classification
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