摘要
HSMC-SVM是一种直接型高速多类支持向量机,适合用于类别较多的分类场合,但由于SMO算法采用经验方法选择工作集,使得在用SMO算法训练HSMC-SVM时,收敛速度较慢。为提高HSMC-SVM的收敛速度,该文提出用基于二次逼近的可行方向法来训练HSMC-SVM,并使用了样本缩减策略。实验表明,这种方法可以有效提高HSMC-SVM的收敛速度,其收敛速度已经超过了基于libsvm的组合多类支持向量机,完全可以用于分类类别多、样本数量大的分类场合。
HSMC-SVM is a kind of high-speed multi-class SVM with direct mode, and it is appropriate for the situation having lots of categories. Because working set selection of SMO algorithm is based on experience, HSMC-SVM would converge slowly trained with SMO. For accelerating the convergence process of HSMC-SVM, a new approach of working set selection based on second order approximation is proposed. At the same time, shrinking strategy is used too. The numeric experiments show that these measures can speed up the convergence process of HSMC-SVM efficiently. The convergence process of HSMC-SVM is even shorter than these composed multi-class SVMs trained with libsvm. Hence, HSMV-SVM based on second order approximation is very appropriate for the situation that classification category is more and the number of training samples is large.
出处
《电子与信息学报》
EI
CSCD
北大核心
2008年第11期2746-2749,共4页
Journal of Electronics & Information Technology
关键词
超球体多类支持向量机
SMO训练算法
工作集选择:二次逼近
Hyper-Sphere Multi-Class SVM（HSMC-SVM）
Sequential Minimization Optimization（SMO） training algorithm
Working set selection
Second Order Approximation（SOA）
