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第4期 王召新,等:基于级联宽度学习的多模态材质识别 ·793· 参考文献: [12]LIU Z,CHEN CL P.Broad learning system:Structural extensions on single-layer and multi-layer neural net- [1]BELL S.UPCHERCH P.SNAVELY N.et al.Material re- works[C]//2017 International Conference on Security cognition in the wild with the materials in context data- Pattern Analysis,and Cybernetics.Shenzhen,China base[C]//Proceedings of the IEEE Conference on Com- 2017:136-141. puter Vision and Pattern Recognition.Massachusetts,Bo- [13]JIN J.LIU Z,CHEN C L P.Discriminative graph regular- ston,2015:3479-3487. ized broad learning system for image recognition[J].Sci- [2]齐静,徐坤,丁希仑.机器人视觉手势交互技术研究进 ence China information sciences,2018.61(11):112209. 展[U.机器人,2017,39(4):565-584. 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Material re￾cognition in the wild with the materials in context data￾base[C]//Proceedings of the IEEE Conference on Com￾puter Vision and Pattern Recognition. Massachusetts, Bo￾ston, 2015: 3479−3487. [1] 齐静, 徐坤, 丁希仑. 机器人视觉手势交互技术研究进 展 [J]. 机器人, 2017, 39(4): 565–584. QI Jing, XU Kun, DING Xilun. Vision-based hand gesture recognition for human-robot interaction: a review[J]. Ro￾bot, 2017, 39(4): 565–584. [2] 吴钟强, 张耀文, 商琳. 基于语义特征的多视图情感分类 方法 [J]. 智能系统学报, 2017, 12(5): 167–173. WU Zhongqiang, ZHANG Yaowen, SHANG Lin. Multi￾view sentiment classification of microblogs based on se￾mantic features[J]. CAAI transactions on intelligent sys￾tems, 2017, 12(5): 167–173. [3] 温有福, 贾彩燕, 陈智能. 一种多模态融合的网络视频相 关性度量方法 [J]. 智能系统学报, 2016, 11(3): 359–365. WEN Youfu, JIA Caiyan, CHEN Zhineng. A multi-modal fusion approach for measuring web video relatedness[J]. CAAI transactions on intelligent systems, 2016, 11(3): 359–365. [4] 马蕊, 刘华平, 孙富春, 等. 基于触觉序列的物体分类 [J]. 智能系统学报, 2015, 10(3): 362–368. MA Rui, LIU Huaping, SUN Fuchun, et al. Object classi￾fication based on the tactile sequence[J]. CAAI transac￾tions on intelligent systems, 2015, 10(3): 362–368. [5] LIU H, WU Y, SUN F, et al. Weakly paired multimodal fusion for object recognition[J]. IEEE transactions on auto￾mation science and engineering, 2017, 15(2): 784–795. [6] EGUÍLUZ A G, RAÑÓ I, Coleman S A, et al. A multi￾modal approach to continuous material identification through tactile sensing[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Daejeon, Korea, 2016: 4912−4917. [7] ZHENG H, FANG L, JI M, et al. Deep learning for sur￾face material classification using haptic and visual inform￾ation[J]. IEEE transactions on multimedia, 2016, 18(12): 2407–2416. [8] ERICKSON Z, CHERNOVA S, KEMP C. Semi-super￾vised haptic material recognition for robots using generat￾ive adversarial networks[J]. arXiv: 1707.02796, 2017. [9] CHEN C L P, LIU Z. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture[J]. IEEE transactions on neural networks and learning systems, 2017, 29(1): 10–24. [10] LIU Z, ZHOU J, CHEN C L P. Broad learning system: Feature extraction based on K-means clustering al￾gorithm[C]//2017 4th International Conference on Inform￾ation, Cybernetics and Computational Social Systems. London, UK, 2017: 683−687. [11] LIU Z, CHEN C L P. Broad learning system: Structural extensions on single-layer and multi-layer neural net￾works[C]//2017 International Conference on Security, Pattern Analysis, and Cybernetics. Shenzhen, China, 2017: 136−141. [12] JIN J, LIU Z, CHEN C L P. Discriminative graph regular￾ized broad learning system for image recognition[J]. Sci￾ence China information sciences, 2018, 61(11): 112209. [13] CHEN C L P, LIU Z, FENG S. Universal approximation capability of broad learning system and its structural vari￾ations[J]. IEEE transactions on neural networks and learn￾ing systems, 2018, 30(4): 1191–1204. [14] LI D, SHUJUAN J, CHUNJIN Z. Improved broad learn￾ing system: partial weights modification based on BP al￾gorithm[J]. Materials science and engineering, 2018, 439(3): 032083. [15] ZHANG T L, CHEN R, YANG X, et al. Rich feature combination for cost-based broad learning system[J]. IEEE access, 2018, 7(1): 160–172. [16] ZHAO H, ZHENG J, DENG W, et al. Semi-supervised broad learning system based on manifold regularization and broad network[J]. IEEE transactions on circuits and systems I: regular papers, 2020, 67(3): 983–994. [17] KONG Y, WANG X, CHENG Y, et al. Hyperspectral im￾agery classification based on semi-supervised broad learn￾ing system[J]. Remote sensing, 2018, 10(5): 685. [18] FENG S, CHEN C L P. Fuzzy broad learning system: A novel neuro-fuzzy model for regression and classification[J]. IEEE transactions on cybernetics, 2018, 50(2): 414–424. [19] JIN J, CHEN C L P. Regularized robust broad learning system for uncertain data modeling[J]. Neurocomputing, 2018, 322(1): 58–69. [20] LIU Z, SHEN Y, LAKSHMINARASIMHAN V B, et al. Efficient low-rank multimodal fusion with modality-spe￾cific factors[J]. arXiv: 1806.00064, 2018. [21] 魏洁. 深度极限学习机的研究与应用 [D]. 太原: 太原理 工大学, 2016. WEI Jie. Research and application of deep extreme learn￾ing machine[D]. Taiyuan: Taiyuan University of Techno￾logy, 2016. [22] ERICKSON Z, LUSKEY N, CHERNOVA S, et al. Clas￾sification of household materials via spectroscopy[J]. IEEE robotics and automation letters, 2019, 4(2): 700–707. [23] ZHENG W, LIU H, WANG B, et al. Cross-modal sur￾face material retrieval using discriminant adversarial learning[J]. IEEE transactions on industrial informatics, 2019(1): 1–1. [24] 贾晨, 刘华平, 续欣莹, 等. 基于宽度学习方法的多模态 信息融合 [J]. 智能系统学报, 2019, 14(1): 154–161. JIA Chen, LIU Huaping, XU Xinying, et al. Multi-modal information fusion based on broad learning method[J]. [25] 第 4 期 王召新,等:基于级联宽度学习的多模态材质识别 ·793·
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