A global Optimization of SVM batch active learning
We consider the problem of SVM batch active learning, which involves distinguishing samples chosen and maximum approximate the real normal vector w in feature space. Although several studies are devoted to batch mode active learning, they suffer either from the uncertain parameter set or from the solutions of local optimization. We introduce a new algorithm for performing batch active learning by cluster diversity and most possibly error approximate method. Experimental results showing that employing our active learning method can significantly reduce the computational cost as well as excellent learning performance in comparison with other active learning methods.
batch active earning cluster diversity global optimization
Xiaojian Ding Yinliang Zhao Yuancheng Li
School of Electronic and Information Engineering,Xian Jiaotong University,Xian,Shaanxi,710049,P.R.China
国际会议
上海
英文
500-503
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）