Page 354 - 《软件学报》2025年第10期

梁志贞 等: 面向集值数据的孪生支持函数机 4751

j.neunet.2022.02.007]
[19] Zhu YW, Cao YR, Xue, Q, Wu QH, Zhang YS. Heavy hitter identification over large-domain set-valued data with local differential
privacy. IEEE Trans. on Information Forensics and Security, 2024, 19: 414–426. [doi: 10.1109/TIFS.2023.3324726]
[20] Cao FY, Huang JZ, Liang JY, Zhao XW, Meng YF, Feng K, Qian YH. An algorithm for clustering categorical data with set-valued
features. IEEE Trans. on Neural Networks and Learning Systems, 2018, 29(10): 4593–4606. [doi: 10.1109/TNNLS.2017.2770167]
[21] Chen YS, Li JJ, Lin RD, Chen DX, Huang ZH. Multi-scale set value decision information system. Control and Decision, 2022, 37(2):
455–463 (in Chinese with English abstract). [doi: 10.13195/j.kzyjc.2020.0882]
[22] Hu J, Chen Y, Zhang QH, Wang GY. Optimal scale selection for generalized multi-scale set-valued decision systems. Journal of
Computer Research and Development, 2022, 59(9): 2027–2038 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.
20210196]
[23] Shivaswamy PK, Bhattacharyya C, Smola AJ. Second order cone programming approaches for handling missing and uncertain data.
Journal of Machine Learning Research, 2006, 7: 1283–1314.
[24] Tzelepis C, Mezaris V, Patras I. Linear maximum margin classifier for learning from uncertain data. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 2018, 40(12): 2948–2962. [doi: 10.1109/tpami.2017.2772235]
[25] Liang ZZ, Zhang L. Uncertainty-aware twin support vector machines. Pattern Recognition, 2022, 129: 108706. [doi: 10.1016/j.patcog.
2022.108706]
[26] Liang ZZ, Ding SF. Fuzzy twin support vector machines with distribution inputs. IEEE Trans. on Fuzzy Systems, 2024, 32(1): 240–254.
[doi: 10.1109/tfuzz.2023.3296503]
[27] Muandet K, Fukumizu K, Dinuzzo F, Schölkopf B. Learning from distributions via support measure machines. In: Proc. of the 25th Int’l
Conf. on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc., 2012, 10–18. [doi: 10.5555/2999134.2999136]
[28] Chen JQ, Hu QH, Xue XP, Ha MH, Ma LT. Support function machine for set-based classification with application to water quality
evaluation. Information Sciences, 2017, 388–389: 48–61. [doi: 10.1016/j.ins.2017.01.001]
[29] Chen JQ, Xue XP, Ma LT, Ha MH. Separability of set-valued data sets and existence of support hyperplanes in the support function
machine. Information Sciences, 2018, 430–431: 432–443. [doi: 10.1016/j.ins.2017.11.057]
[30] Royden HL, Fitzpatrick PM. Real Analysis. 5th ed., London: Pearson, 2022.
[31] Chen JQ, Hu QH, Xue XP, Ha MH, Ma LT, Zhang XC, Yu ZP. Possibility measure based fuzzy support function machine for set-based
fuzzy classifications. Information Sciences, 2019, 483: 192–205. [doi: 10.1016/j.ins.2019.01.022]
[32] Komornik V. Spaces of continuous functions. In: Komornik V, ed. Lectures on Functional Analysis and the Lebesgue Integral. London:
Springer, 2016. 257–304. [doi: 10.1007/978-1-4471-6811-9_8]
[33] Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. London:
Cambridge University Press, 2000
[34] Aggarwal CC. Yu PS. Outlier detection with uncertain data. In: Proc. of the 2008 SIAM Int’l Conf. on Data Mining. Atlanta: SIAM,
2008. 483–493. [doi: 10.1137/1.9781611972788.44]
[35] Hu YQ, Mian AS, Owens R. Sparse approximated nearest points for image set classification. In: Proc. of the 2011 Conf. on Computer
Vision and Pattern Recognition. Colorado: IEEE, 2011. 121–128. [doi: 10.1109/cvpr.2011.5995500]
[36] Zhu PF, Zuo WM, Zhang L, Shiu SCK, Zhang D. Image set-based collaborative representation for face recognition. IEEE Trans. on
Information Forensics and Security, 2014, 9(7): 1120–1132. [doi: 10.1109/TIFS.2014.2324277]
[37] Demšar J. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 2006, 7: 1–30.
[38] Yang JC, Shi R, Ni BB. MedMNIST classification Decathlon: A lightweight AutoML benchmark for medical image analysis. In: Proc. of
the 18th IEEE Int’l Symp. on Biomedical Imaging. Nice: IEEE, 2021. 191–195. [doi: 10.1109/isbi48211.2021.9434062]

附中文参考文献:
[7] 丁世飞, 张健, 张谢锴, 安悦瑄. 多分类孪生支持向量机研究进展. 软件学报, 2018, 29(1): 89–108. http://www.jos.org.cn/1000-9825/
5319.htm [doi: 10.13328/j.cnki.jos.005319]
[21] 陈应生, 李进金, 林荣德, 陈东晓, 黄哲煌. 多尺度集值决策信息系统. 控制与决策, 2022, 37(2): 455–463. [doi: 10.13195/j.kzyjc.
2020.0882]
[22] 胡军, 陈艳, 张清华, 王国胤. 广义多尺度集值决策系统最优尺度选择. 计算机研究与发展, 2022, 59(9): 2027–2038. [doi:
10.7544/issn1000-1239.20210196]