Share Email Print

Proceedings Paper

Research on methodology of document classification based on generalized learning
Author(s): Min Yao; Zhiwei Jiang; Xiaogan Jing; Wensheng Yi
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 3 November 2005
PDF: 7 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60432E (3 November 2005); doi: 10.1117/12.655008
Show Author Affiliations
Min Yao, Zhejiang Univ. (China)
Nanjing Univ. (China)
Zhiwei Jiang, Zhejiang Univ. (China)
Nanjing Univ. (China)
Xiaogan Jing, Nanjing Univ. (China)
Wensheng Yi, Nanjing Univ. (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

© SPIE. Terms of Use
Back to Top