TY - GEN
T1 - Support vector machines based on weighted scatter degree
AU - Jin, A-Long
AU - Zhou, Xin
AU - Ye, Chizhou
PY - 2011
Y1 - 2011
N2 - Support Vector Machines (SVMs) are efficient tools, which have been widely studied and used in many fields. However, original SVM (C-SVM) only focuses on the scatter between classes, but neglects the global information about the data which are also vital for an optimal classifier. Therefore, C-SVM loses some robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according to the global characteristics of the data. However, parts of existing work using this approach are based on specific distribution assumption (S-SVM), while the rest fail to utilize the global information (GS-SVM). In this paper, we propose a simple but efficient method based on weighted scatter degree (WSD-SVM) to embed the global information into GS-SVM without any distribution assumptions. A comparison of WSD-SVM, C-SVM and GS-SVM is conducted, and the results on several data sets show the advantages of WSD-SVM.
AB - Support Vector Machines (SVMs) are efficient tools, which have been widely studied and used in many fields. However, original SVM (C-SVM) only focuses on the scatter between classes, but neglects the global information about the data which are also vital for an optimal classifier. Therefore, C-SVM loses some robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according to the global characteristics of the data. However, parts of existing work using this approach are based on specific distribution assumption (S-SVM), while the rest fail to utilize the global information (GS-SVM). In this paper, we propose a simple but efficient method based on weighted scatter degree (WSD-SVM) to embed the global information into GS-SVM without any distribution assumptions. A comparison of WSD-SVM, C-SVM and GS-SVM is conducted, and the results on several data sets show the advantages of WSD-SVM.
KW - Hyperplane
KW - Large Margin Machines
KW - Scatter Degree
KW - Support Vector Machines
KW - Translation
UR - http://www.scopus.com/inward/record.url?scp=80054071425&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23896-3_77
DO - 10.1007/978-3-642-23896-3_77
M3 - Conference Proceeding
AN - SCOPUS:80054071425
SN - 9783642238956
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 620
EP - 629
BT - Artificial Intelligence and Computational Intelligence - Third International Conference, AICI 2011, Proceedings
T2 - 3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011
Y2 - 24 September 2011 through 25 September 2011
ER -