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Ordinal regression based on learning vector quantization
Tang FZ(唐凤珍); Tio, Peter
作者部门机器人学研究室
关键词Ordinal Regression Learning Vector Quantization Generalized Matrix Learning Vector Quantization
发表期刊Neural Networks
ISSN0893-6080
2017
卷号93页码:76-88
收录类别SCI ; EI
EI收录号20172203698032
WOS记录号WOS:000406784500007
产权排序1
资助机构EPSRC grant EP/L000296/1
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.
语种英语
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
WOS类目Computer Science, Artificial Intelligence ; Neurosciences
关键词[WOS]CLASSIFICATION
WOS研究方向Computer Science ; Neurosciences & Neurology
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/20497
专题机器人学研究室
通讯作者Tang FZ(唐凤珍)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street, Shenyang, Liaoning Province, 110016, China
2.School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
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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.
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