Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructe...Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.展开更多
提出一种基于双支持向量机的偏二叉树多类分类算法,偏二叉树双支持向量机多类分类算法.该算法综合了二叉树支持向量机和双支持向量机的优势,实现了在不降低分类性能的前提下,大大缩短训练时间.理论分析和UCI(University of California I...提出一种基于双支持向量机的偏二叉树多类分类算法,偏二叉树双支持向量机多类分类算法.该算法综合了二叉树支持向量机和双支持向量机的优势,实现了在不降低分类性能的前提下,大大缩短训练时间.理论分析和UCI(University of California Irvine)机器学习数据库数据集上的实验结果共同证明,偏二叉树双支持向量机多类分类算法在训练时间上具有绝对的优势,尤其在处理稍大数据集的多类分类问题时,这一优势尤为突出;实验仿真结果还证明,在采用非线性核时,该算法取得了比基于经典支持向量机的一对其余多类分类算法及二叉树支持向量机更好的分类效果;同时该算法还解决了后两种算法可能存在的样本不平衡问题,以及基于经典支持向量机的一对其余多类分类算法可能存在的不可分区域问题.展开更多
文摘Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.
文摘提出一种基于双支持向量机的偏二叉树多类分类算法,偏二叉树双支持向量机多类分类算法.该算法综合了二叉树支持向量机和双支持向量机的优势,实现了在不降低分类性能的前提下,大大缩短训练时间.理论分析和UCI(University of California Irvine)机器学习数据库数据集上的实验结果共同证明,偏二叉树双支持向量机多类分类算法在训练时间上具有绝对的优势,尤其在处理稍大数据集的多类分类问题时,这一优势尤为突出;实验仿真结果还证明,在采用非线性核时,该算法取得了比基于经典支持向量机的一对其余多类分类算法及二叉树支持向量机更好的分类效果;同时该算法还解决了后两种算法可能存在的样本不平衡问题,以及基于经典支持向量机的一对其余多类分类算法可能存在的不可分区域问题.
