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.