Working Set Selection Using Second Order Information for Training Support Vector Machines
Working Set Selection Using Second Order Information for Training Support Vector Machines
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发表时间：
2005-12
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通讯作者：
Rong-En Fan;Pai-Hsuen Chen;Chih-Jen Lin
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作者：
Rong-En Fan;Pai-Hsuen Chen;Chih-Jen Lin
工作集选择是训练支持向量机 (SVM) 分解方法的重要步骤。本文开发了一种用于 SMO 型分解方法中工作集选择的新技术。它利用二阶信息来实现快速收敛。建立了线性收敛等理论特性。实验表明，所提出的方法比现有的使用一阶信息的选择方法更快。
Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in SMO-type decomposition methods. It uses second order information to achieve fast convergence. Theoretical properties such as linear convergence are established. Experiments demonstrate that the proposed method is faster than existing selection methods using first order information.