Ordinal regression based on learning vector quantization
文献类型：期刊论文
| 作者 | Tang FZ(唐凤珍) ; Tio, Peter
|
| 刊名 | Neural Networks
 |
| 出版日期 | 2017 |
| 卷号 | 93页码:76-88 |
| 关键词 | Ordinal regression Learning vector quantization Generalized matrix learning vector quantization |
| ISSN号 | 0893-6080 |
| 产权排序 | 1 |
| 通讯作者 | Tang FZ(唐凤珍) |
| 中文摘要 | Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to more intuitive parameter update rules. Moreover, in our approach the bandwidth of the prototype weights is automatically adapted. Empirical investigation on a number of datasets reveals that overall the proposed approach tends to have superior out-of-sample performance, when compared to alternative ordinal regression methods. |
| WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Neurosciences |
| 研究领域[WOS] | Computer Science ; Neurosciences & Neurology |
| 关键词[WOS] | CLASSIFICATION |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000406784500007 |
| 源URL | [http://ir.sia.cn/handle/173321/20497]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 作者单位 | 1.School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom 2.Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street, Shenyang, Liaoning Province, 110016, China |
| 推荐引用方式 GB/T 7714 | Tang FZ,Tio, Peter. Ordinal regression based on learning vector quantization[J]. Neural Networks,2017,93:76-88. |
| APA | Tang FZ,&Tio, Peter.(2017).Ordinal regression based on learning vector quantization.Neural Networks,93,76-88. |
| MLA | Tang FZ,et al."Ordinal regression based on learning vector quantization".Neural Networks 93(2017):76-88. |
入库方式： OAI收割
来源：沈阳自动化研究所
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