DocumentCode
2222746
Title
Support Vector Machine for Suppressing Error Method
Author
Lu, Shuxia ; Shi, Pu ; Chen, Ming
Author_Institution
Key Lab. of Machine Learning & Comput. Intell., Hebei Univ., Baoding, China
fYear
2009
fDate
26-28 Dec. 2009
Firstpage
413
Lastpage
416
Abstract
Support Vector Machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.
Keywords
error analysis; noise; support vector machines; noises; outliers; separation hypersurface; support vector machine; suppressing error method; Computer errors; Computer science; Educational institutions; Information science; Machine learning; Mathematics; Noise reduction; Support vector machine classification; Support vector machines; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4909-5
Type
conf
DOI
10.1109/ICISE.2009.1149
Filename
5455125
Link To Document