Identifying knowledge domain and incremental new class learning in SVM

Author

Jia, Hongbin ; Murphey, Yi Lu ; Gutchess, Daniel ; Chang, Tzyy-Shuh

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA

Volume

5

fYear

2005

fDate

31 July-4 Aug. 2005

Firstpage

2742

Abstract

An incremental class learning system for support vector machine (SVM) is presented for learning new knowledge from newly available data without forgetting the existing knowledge. We present algorithms for knowledge domain description, new knowledge detection, and incremental learning of new class knowledge. We have applied the incremental learning system to a data set provided by the UCI machine learning Web site, and the results show that the proposed SVM incremental class learning system is quite effective.

Keywords

learning (artificial intelligence); learning systems; support vector machines; incremental class learning system; incremental learning; knowledge domain description; knowledge domain identification; new knowledge detection; support vector machine; Data engineering; Information retrieval; Inspection; Intelligent systems; Iterative algorithms; Learning systems; Machine learning; Support vector machine classification; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on

Print_ISBN

0-7803-9048-2

Type

conf

DOI

10.1109/IJCNN.2005.1556359

Filename

1556359