Frame (networking)

Statistical learning theory

Mathematics

Support vector machine

Data mining

Statistics

Linear classifier

Machine learning

Artificial intelligence

Set (abstract data type)

Programming language

Computer science

Document classification

Flexibility (engineering)

One-class classification

Fuzzy logic

Telecommunications

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

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.

Research on methodology of document classification based on generalized learning