Online independent Lagrangian support vector machine

摘要：

In this paper, a novel incremental learning method called online independent Lagrangian support vector machine (OILSVM) is proposed. It achieves comparable classification accuracy with benchmark Lagrangian support vector machine (LSVM), while still enjoying the time efficiency of online learning machines. As opposed to the newly proposed OLSVM that utilizes the KKT conditions as data selection strategy, the size of the solution obtained by OILSVM using a linear independence check is always bounded, which implies bounded memory requirements, training and testing time. Experimental results demonstrate the effectiveness of the proposed OILSVM.

关键词： online learning Lagrangian support vector machine linear independence check

作者: Yu Jin Hongbing Ji Lei Wang Lin Lin

作者单位: School of Electronic Engineering, Xidian University, Xian, China

会议类型: 国际会议

会议名称: 第七届多光谱图象处理与模式识别国际学术会议

会议地点: 桂林

会议语种:英文

页码: 1-8

在线出版日期: 2011-11-01（万方平台首次上网日期，不代表论文的发表时间）