中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 人工智能与机器学习
Sample-based online learning for bi-regular hinge loss

文献类型：期刊论文

作者Xue, Wei1,2,3; Zhong, Ping1; Zhang, Wensheng4; Yu, Gaohang5; Chen, Yebin2
刊名INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
出版日期2021-01-24
页码16
关键词SVM Max-margin classification Hinge loss Elastic net Online learning
ISSN号1868-8071
DOI10.1007/s13042-020-01272-7
通讯作者Xue, Wei(cswxue@ahut.edu.cn) ; Zhong, Ping(zhongping@nudt.edu.cn)
英文摘要Support vector machine (SVM), a state-of-the-art classifier for supervised classification task, is famous for its strong generalization guarantees derived from the max-margin property. In this paper, we focus on the maximum margin classification problem cast by SVM and study the bi-regular hinge loss model, which not only performs feature selection but tends to select highly correlated features together. To solve this model, we propose an online learning algorithm that aims at solving a non-smooth minimization problem by alternating iterative mechanism. Basically, the proposed algorithm alternates between intrusion samples detection and iterative optimization, and at each iteration it obtains a closed-form solution to the model. In theory, we prove that the proposed algorithm achieves O(1/root T) convergence rate under some mild conditions, where T is the number of training samples received in online learning. Experimental results on synthetic data and benchmark datasets demonstrate the effectiveness and performance of our approach in comparison with several popular algorithms, such as LIBSVM, SGD, PEGASOS, SVRG, etc.
资助项目National Natural Science Foundation of China[12071104] ; National Natural Science Foundation of China[61671456] ; National Natural Science Foundation of China[61806004] ; National Natural Science Foundation of China[61971428] ; China Postdoctoral Science Foundation[2020T130767] ; Natural Science Foundation of the Anhui Higher Education Institutions of China[KJ2019A0082] ; Natural Science Foundation of Zhejiang Province, China[LD19A010002]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000610860500001
出版者SPRINGER HEIDELBERG
资助机构National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Natural Science Foundation of the Anhui Higher Education Institutions of China ; Natural Science Foundation of Zhejiang Province, China
源URL[http://ir.ia.ac.cn/handle/173211/42880]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Xue, Wei; Zhong, Ping
作者单位1.Natl Univ Defense Technol, Natl Key Lab Sci & Technol Automatic Target Recog, Changsha 410073, Peoples R China
2.Anhui Univ Technol, Sch Comp Sci & Technol, Maanshan 243032, Peoples R China
3.Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 211189, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
5.Hangzhou Dianzi Univ, Sch Sci, Hangzhou 310018, Peoples R China
推荐引用方式
GB/T 7714		 Xue, Wei,Zhong, Ping,Zhang, Wensheng,et al. Sample-based online learning for bi-regular hinge loss[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2021:16.
APA Xue, Wei,Zhong, Ping,Zhang, Wensheng,Yu, Gaohang,&Chen, Yebin.(2021).Sample-based online learning for bi-regular hinge loss.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,16.
MLA Xue, Wei,et al."Sample-based online learning for bi-regular hinge loss".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2021):16.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。