Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multi-category Classification | |
Wang, Lingfeng; Zhang, Xuyao; Pan, Chunhong | |
发表期刊 | IEEE Transactions on Neural Networks and Learning Systems |
2015-10 | |
页码 | 1-7 |
摘要 | In this brief, we propose a new margin scalable discriminative least squares regression (MSDLSR) model for multicategory classification. The main motivation behind the MSDLSR is to explicitly control the margin of DLSR model. We first prove that the DLSR is a relaxation of the traditional L₂-support vector machine. Based on this fact, we further provide a theorem on the margin of DLSR. With this theorem, we add an explicit constraint on DLSR to restrict the number of zeros of dragging values, so as to control the margin of DLSR. The new model is called MSDLSR. Theoretically, we analyze the determination of the margin and support vectors of MSDLSR. Extensive experiments illustrate that our method outperforms the current state-of-the-art approaches on various machine leaning and real-world data sets. |
关键词 | Discriminative Least Squares Regression (Dlsr) Least Squares Regression (Lsr) Multicategory Classification. |
收录类别 | SCI |
语种 | 英语 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/10723 |
专题 | 模式识别国家重点实验室_先进时空数据分析与学习 |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang, Lingfeng,Zhang, Xuyao,Pan, Chunhong. MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multi-category Classification[J]. IEEE Transactions on Neural Networks and Learning Systems,2015:1-7. |
APA | Wang, Lingfeng,Zhang, Xuyao,&Pan, Chunhong.(2015).MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multi-category Classification.IEEE Transactions on Neural Networks and Learning Systems,1-7. |
MLA | Wang, Lingfeng,et al."MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multi-category Classification".IEEE Transactions on Neural Networks and Learning Systems (2015):1-7. |
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