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Xiaohan Yang, Xiaojuan Li, Yong Guan, et al. Overfitting reduction of pose estimation for deep learning visual odometry. China Communications, 2020, 17(6): 196–210.YangXiaohanLiXiaojuanGuanYongOverfitting reduction of pose estimation for deep learning visual odometry2020176196210Search in Google Scholar
Cha K H, Petrick N, Pezeshk A, et al. Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images. (Conference Paper), Progress in Biomedical Optics and Imaging, Proceedings of SPIE, 2019, 188–194.ChaK HPetrickNPezeshkA(Conference Paper), Progress in Biomedical Optics and Imaging, Proceedings of SPIE2019188194Search in Google Scholar
Gonzalez German, Ash Samuel Y, Estepar Raul San Jose. Reply to Mummadi et al.: Overfitting and use of mismatched cohorts in deep learning models: preventable design limitations. American Journal of Respiratory and Critical Care Medicine, 2018, 198(4): 545–555.GonzalezGermanAsh SamuelYEsteparRaul San JoseReply to Mummadi et al.: Overfitting and use of mismatched cohorts in deep learning models: preventable design limitations20181984545555Search in Google Scholar
Ashiquzzaman A, Tushar A K, Islam M R, et al. Reduction of overfitting in diabetes prediction using deep learning neural network. IT Convergence and Security, 2017, 2018, 449: 35–43.AshiquzzamanATusharA KIslamM RReduction of overfitting in diabetes prediction using deep learning neural network2017, 20184493543Search in Google Scholar
Shun Zhang, Yihong Gong, Jinjun Wang. The development of deep convolutional neural networks and their applications in the field of computer vision. Chinese Journal of Computers, 2019, 42(03): 453–482. DOI:10.11897/SP.J.1016.2019.00453.ZhangShunGongYihongWangJinjunThe development of deep convolutional neural networks and their applications in the field of computer vision2019420345348210.11897/SP.J.1016.2019.00453Open DOISearch in Google Scholar
Gong T, Fan T, Guo J, et al. GPU-based parallel optimization of immune convolutional neural network and embedded system. Engineering Applications of Artificial Intelligence, 2016, 36(25): 226–238.GongTFanTGuoJGPU-based parallel optimization of immune convolutional neural network and embedded system20163625226238Search in Google Scholar
Danfeng Liu, Jianxia Liu. Neural network model for deep learning overfitting problem. Journal of Natural Science of Xiangtan University, 2018, 40(2): 96–99.LiuDanfengLiuJianxiaNeural network model for deep learning overfitting problem20184029699Search in Google Scholar
Junhua Cheng, Guohui Zeng, Dunke Lu, et al. Dropout-based improved convolutional neural network model averaging method. Journal of Computer Applications, 2019, 39(06): 1601–1606.ChengJunhuaZengGuohuiLuDunkeDropout-based improved convolutional neural network model averaging method2019390616011606Search in Google Scholar
Jianchuan Li, Guojun Qin, Xisen Wen, Fuqing Hu. The overfitting problem of neural network learning algorithm and its solution. Vibration, Testing and Diagnosis, 2002, (04): 16–20+76.LiJianchuanQinGuojunWenXisenHuFuqingThe overfitting problem of neural network learning algorithm and its solution2002041620+76Search in Google Scholar
Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 2014, 15(1): 1929–1958.SrivastavaNHintonGKrizhevskyADropout: A simple way to prevent neural networks from overfitting201415119291958Search in Google Scholar
Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detector. arXiv Preprint, 2012, 2012: arXiv. 1207.0580.HintonG ESrivastavaNKrizhevskyAImproving neural networks by preventing co-adaptation of feature detector20122012arXiv. 1207.0580.Search in Google Scholar
Dawei Chen, Zhao Yan, Haoyan Liu. Overfitting phenomenon of SVD series algorithms in scoring prediction. Journal of Shandong University (Engineering Science Edition), 2014, 44(03): 15–21.ChenDaweiYanZhaoLiuHaoyanOverfitting phenomenon of SVD series algorithms in scoring prediction201444031521Search in Google Scholar
Weiwei Shen, Ying Li, Zhihao Yang, et al. Attribute reduction to prevent overfitting. Application Research of Computers, 2020, 37(09): 2665–2668.ShenWeiweiLiYingYangZhihaoAttribute reduction to prevent overfitting2020370926652668Search in Google Scholar
Guanghua Qin, Zuoyong Li. Research and application of BP network over-fitting problem. Journal of Wuhan University (Engineering Science Edition), 2006, (06): 55–58.QinGuanghuaLiZuoyongResearch and application of BP network over-fitting problem2006065558Search in Google Scholar
Zheng Yang, Fan Gao, Songnian Fu, et al. Overfitting effect of artificial neural network based nonlinear equalizer, mathematical origin to transmission evolution. 2020, 63(6): 82–97. DOI:10.1007/s11432-020-2873-x.YangZhengGaoFanFuSongnianOverfitting effect of artificial neural network based nonlinear equalizer, mathematical origin to transmission evolution2020636829710.1007/s11432-020-2873-xOpen DOISearch in Google Scholar
Chiyuan Zhang, Oriol Vinyals, Remi Munos, Samy Bengio. A study on overfitting in deep reinforcement learning. Statistics, 2018, (2): 1–25.ZhangChiyuanVinyalsOriolMunosRemiBengioSamyA study on overfitting in deep reinforcement learning20182125Search in Google Scholar
Jinzhe Wang, Zeru Wang, Hongmei Wang. Improved CNN algorithm based on PSO algorithm and dropout. Journal of Changchun University of Technology, 2019, 40(01): 26–30.WangJinzheWangZeruWangHongmeiImproved CNN algorithm based on PSO algorithm and dropout201940012630Search in Google Scholar
Mackay DJC. Bayesian interpolation. Neural Computation, 1992, 4(3): 415–447.MackayDJCBayesian interpolation199243415447Search in Google Scholar
Mackay DJC. Bayesian interpolation. Neural Computation, 1992, 4(3): 415–447.MackayDJCBayesian interpolation199243415447Search in Google Scholar
Zeiler M D, Fergus R. Stochastic pooled for regularization of deep convolutional neural networks. https://arxiv.org/pdf/1301.3557.pdf [2018-09-17].ZeilerM DFergusRhttps://arxiv.org/pdf/1301.3557.pdf [2018-09-17].Search in Google Scholar