Page 282 - 《软件学报》2026年第2期
P. 282

杭均一 等: 基于不变性注入的多标记类属特征学习                                                         761


                     CVPR46437.2021.01500]
                 [52]   Srivastava  N,  Hinton  G,  Krizhevsky  A,  Sutskever  I,  Salakhutdinov  R.  Dropout:  A  simple  way  to  prevent  neural  networks  from
                     overfitting. The Journal of Machine Learning Research, 2014, 15(1): 1929–1958.
                 [53]   Huang  G,  Sun  Y,  Liu  Z,  Sedra  D,  Weinberger  KQ.  Deep  networks  with  stochastic  depth.  In:  Proc.  of  the  14th  European  Conf.  on
                     Computer Vision. Amsterdam: Springer, 2016. 646–661. [doi: 10.1007/978-3-319-46493-0_39]
                 [54]   Achille A, Soatto S. Information dropout: Learning optimal representations through noisy computation. IEEE Trans. on Pattern Analysis
                     and Machine Intelligence, 2018, 40(12): 2897–2905. [doi: 10.1109/TPAMI.2017.2784440]
                 [55]   Goodfellow  IJ,  Shlens  J,  Szegedy  C.  Explaining  and  harnessing  adversarial  examples.  In:  Proc.  of  the  3rd  Int’l  Conf.  on  Learning
                     Representations. San Diego, 2015.
                 [56]   Duan RJ, Chen YF, Niu DT, Yang Y, Qin AK, He Y. AdvDrop: Adversarial attack to DNNs by dropping information. In: Proc. of the
                     2021 IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 7486–7495. [doi: 10.1109/ICCV48922.2021.00741]
                 [57]   Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?” Explaining the predictions of any classifier. In: Proc. of the 22nd ACM
                     SIGKDD  Int’l  Conf.  on  Knowledge  Discovery  and  Data  Mining.  San  Francisco:  ACM,  2016.  1135–1144.  [doi:  10.1145/2939672.
                     2939778]
                 [58]   Fong RC, Vedaldi A. Interpretable explanations of black boxes by meaningful perturbation. In: Proc. of the 2017 IEEE Int’l Conf. on
                     Computer Vision. Venice: IEEE, 2017. 3449–3457. [doi: 10.1109/ICCV.2017.371]
                 [59]   Wilcoxon F. Individual comparisons by ranking methods. In: Kotz S, Johnson NL, eds. Breakthroughs in Statistics: Methodology and
                     Distribution. New York: Springer, 1992. 196–202. [doi: 10.1007/978-1-4612-4380-9_16]

                 附中文参考文献
                 [21]   吕沈欢, 陈一赫, 姜远. 多标记学习中基于交互表示的深度森林方法. 软件学报, 2024, 35(4): 1934–1944. http://www.jos.org.cn/1000-
                     9825/6841.htm [doi: 10.13328/j.cnki.jos.006841]
                 [22]   刘军煜, 贾修一. 一种利用关联规则挖掘的多标记分类算法. 软件学报, 2017, 28(11): 2865–2878. http://www.jos.org.cn/1000-9825/
                     5341.htm [doi: 10.13328/j.cnki.jos.005341]

                 作者简介
                 杭均一, 博士生, 主要研究领域为机器学习, 多标记分类.
                 张敏灵, 博士, 教授, 博士生导师, CCF  杰出会员, 主要研究领域为机器学习, 数据挖掘.
   277   278   279   280   281   282   283   284   285   286   287