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Ordinal regression based on learning vector quantization
Tang FZ(唐凤珍); Tio, Peter
Department机器人学研究室
Source PublicationNeural Networks
ISSN0893-6080
2017
Volume93Pages:76-88
Indexed BySCI ; EI
EI Accession number20172203698032
WOS IDWOS:000406784500007
Contribution Rank1
Funding OrganizationEPSRC grant EP/L000296/1
KeywordOrdinal Regression Learning Vector Quantization Generalized Matrix Learning Vector Quantization
AbstractRecently, 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.
Language英语
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS KeywordCLASSIFICATION
WOS Research AreaComputer Science ; Neurosciences & Neurology
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20497
Collection机器人学研究室
Corresponding AuthorTang FZ(唐凤珍)
Affiliation1.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
Recommended Citation
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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