Xiang, Haorui; Wu, Zhichang; Wang, Rong; Nie, Feiping; Li, Xuelong (2025) Robust support vector ordinal regression. Information Sciences, 717. doi:10.1016/j.ins.2025.122277
Reference Type | Journal (article/letter/editorial) | ||
---|---|---|---|
Title | Robust support vector ordinal regression | ||
Journal | Information Sciences | ||
Authors | Xiang, Haorui | Author | |
Wu, Zhichang | Author | ||
Wang, Rong | Author | ||
Nie, Feiping | Author | ||
Li, Xuelong | Author | ||
Year | 2025 (November) | Volume | 717 |
Publisher | Elsevier BV | ||
DOI | doi:10.1016/j.ins.2025.122277Search in ResearchGate | ||
Generate Citation Formats | |||
Mindat Ref. ID | 18441520 | Long-form Identifier | mindat:1:5:18441520:5 |
GUID | 0 | ||
Full Reference | Xiang, Haorui; Wu, Zhichang; Wang, Rong; Nie, Feiping; Li, Xuelong (2025) Robust support vector ordinal regression. Information Sciences, 717. doi:10.1016/j.ins.2025.122277 | ||
Plain Text | Xiang, Haorui; Wu, Zhichang; Wang, Rong; Nie, Feiping; Li, Xuelong (2025) Robust support vector ordinal regression. Information Sciences, 717. doi:10.1016/j.ins.2025.122277 | ||
In | (2025) Information Sciences Vol. 717. Elsevier BV |
References Listed
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Diaz (2019) Soft labels for ordinal regression , 4738 | |
![]() | |
Shin (2022) Moving window regression: a novel approach to ordinal regression , 18760 | |
![]() | |
![]() | |
Li (2022) Ordinalclip: learning rank prompts for language-guided ordinal regression , 35313 | |
![]() | |
![]() | |
Not Yet Imported: - journal-article : 10.1162/neco.2007.19.3.792 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
Zhong (2023) IEEE Trans. Neural Netw. Learn. Syst. Ordinal regression with pinball loss , 1 | |
Lin (2006) Large-margin thresholded ensembles for ordinal regression: theory and practice , 319 | |
Nie (2024) Multi-class support vector machine with maximizing minimum margin vol. 38, 14466 | |
Herbrich (1999) Support vector learning for ordinal regression , 97 | |
Shashua (2002) Adv. Neural Inf. Process. Syst. Ranking with large margin principle: two approaches 15 | |
Not Yet Imported: Neural Computation - journal-article : 10.1162/NECO_a_00265 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
Not Yet Imported: IEEE Transactions on Cybernetics - journal-article : 10.1109/TCYB.2017.2682852 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
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Not Yet Imported: - journal-article : 10.1016/j.eswa.2023.120766 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
Not Yet Imported: - journal-article : 10.1016/j.asoc.2020.106941 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | |
Chu (2005) New approaches to support vector ordinal regression , 145 | |
![]() | |
Not Yet Imported: - journal-article : 10.1109/TNN.2006.875973 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
Not Yet Imported: - journal-article : 10.1214/18-EJS1404 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
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Not Yet Imported: - journal-article : 10.1016/j.neunet.2007.10.003 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
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Chu (2005) J. Mach. Learn. Res. Gaussian processes for ordinal regression 6 | |
![]() | |
Sánchez-Monedero (2019) J. Mach. Learn. Res. Orca: a matlab/octave toolbox for ordinal regression 20, 1 | |
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