Multi-Class SVM for Large Data Sets Considering Models of Classes Distribution

Support Vector Machines (SVM) have gained profound interest amidst the researchers. One of the important issues concerning SVM is with its application to large data sets. It is recognized that SVM is computationally very intensive. This paper presents a novel multi SVM classification approach for large data sets using the sketch of classes distribution which is obtained by using SVM and minimum enclosing ball (MEB) method. Our approach has distinctive advantages on dealing with huge data sets. Experiments done with several large synthetic and real world data sets, show good performance on computational expense and accuracy.

• Type: Conference
• Year: 2008
• Where: DMIN
• Authors: Jair Cervantes, Xiaoou Li, Wen Yu
• Keywords: Data Mining, Data Sets, DMIN 2008, Issues Concerning Svm, Large Data Sets