Learning with smooth Hinge losses
文献类型：期刊论文
| 作者 | Luo, JunRu4,5; Qiao, Hong2,6 ; Zhang, Bo1,3
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2021-11-06 |
| 卷号 | 463页码:379-387 |
| 关键词 | Smooth Hinge loss Convex surrogate loss Support vector machine Trust region Newton method |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2021.08.060 |
| 通讯作者 | Zhang, Bo(b.zhang@amt.ac.cn) |
| 英文摘要 | Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain a faster convergence rate with modern optimization algorithms. In this paper, we introduce two smooth Hinge losses psi(G)(alpha, sigma) and psi(M)(alpha; sigma) which are infinitely differentiable and converge to the Hinge loss uniformly in alpha as sigma tends to 0. By replacing the Hinge loss with these two smooth Hinge losses, we obtain two smooth support vector machines (SSVMs), respectively. Solving the SSVMs with the Trust Region Newton method (TRON) leads to two quadratically convergent algorithms. Experiments in text classification tasks show that the proposed SSVMs are effective in real-world applications. We also introduce a general smooth convex loss function to unify several commonly-used convex loss functions in machine learning. The general framework provides smooth approximation functions to non-smooth convex loss functions, which can be used to obtain smooth models that can be solved with faster convergent optimization algorithms. (C) 2021 Elsevier B.V. All rights reserved. |
| WOS关键词 | SUPPORT VECTOR MACHINE ; FINITE NEWTON METHOD ; CLASSIFICATION ; CONSISTENCY ; MINIMIZATION |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000708073900014 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46206]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Zhang, Bo |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 4.Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou 213164, Jiangsu, Peoples R China 5.Changzhou Univ, Aliyun Sch Big Data, Changzhou 213164, Jiangsu, Peoples R China 6.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, JunRu,Qiao, Hong,Zhang, Bo. Learning with smooth Hinge losses[J]. NEUROCOMPUTING,2021,463:379-387. |
| APA | Luo, JunRu,Qiao, Hong,&Zhang, Bo.(2021).Learning with smooth Hinge losses.NEUROCOMPUTING,463,379-387. |
| MLA | Luo, JunRu,et al."Learning with smooth Hinge losses".NEUROCOMPUTING 463(2021):379-387. |
入库方式： OAI收割
来源：自动化研究所
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