Research on methodology of document classification based on generalized learning

Min Yao; Zhiwei Jiang; Xiaogan Jing; Wensheng Yi

doi:10.1117/12.655008

3 November 2005 Research on methodology of document classification based on generalized learning

Min Yao, Zhiwei Jiang, Xiaogan Jing, Wensheng Yi

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 60432E (2005) https://doi.org/10.1117/12.655008
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

Document classification is one of important steps in document mining. In this paper, we present a new kind of document classification method based on generalized learning model (GLM for short). GLM is an extensible machine learning model with great flexibility. It may fuses symbolic learning, fuzzy learning, statistical learning, and neural learning together. If necessary, new learning model can be incorporated. To describe and represent documents more reasonably, we develop a approach to extract membership vector as features of documents. In view of the characteristics of document classification, two kinds of document classification methods are employed under GLM frame. One is based on fuzzy set theory, the other is based on support vector machine (SVM). These two kinds of methods can supplement each other to achieve better performance.

Citation Download Citation

Min Yao, Zhiwei Jiang, Xiaogan Jing, and Wensheng Yi "Research on methodology of document classification based on generalized learning", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60432E (3 November 2005); https://doi.org/10.1117/12.655008

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KEYWORDS

Fuzzy logic

Machine learning

Classification systems

Mining

Binary data

Feature extraction

Cognitive modeling

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