WANG C, ZHANG Y L, GU M L. Target Tracking and Classification Algorithm for Adaptive Cruise Control System Via Internet Technology[J]. Wireless Personal Communications, 2018, 102(2):1307-1326. doi: 10.1007/s11277-017-5196-x
王莉军.海量数据下的文本信息检索算法仿真分析[J].计算机仿真, 2016, 33(4):429-432. doi: 10.3969/j.issn.1006-9348.2016.04.095
欧石燕, 唐振贵, 苏翡斐.面向信息检索的术语服务构建与应用研究[J].中国图书馆学报, 2016, 42(2):32-51.
TAJEDDINE R, GNILKE O W, EL ROUAYHEB S. Private Information Retrieval from MDS Coded Data in Distributed Storage Systems[J]. IEEE Transactions on Information Theory, 2018, 64(11):7081-7093. doi: 10.1109/TIT.2018.2815607
叶卫根, 宋威.融合信任用户间接影响的个性化推荐算法[J].计算机工程与科学, 2016, 38(12):2579-2586. doi: 10.3969/j.issn.1007-130X.2016.12.027
李桃迎, 李墨, 李鹏辉.基于加权Slope One的协同过滤个性化推荐算法[J].计算机应用研究, 2017, 34(8):2264-2268. doi: 10.3969/j.issn.1001-3695.2017.08.005
ZHANG L P, ZHANG L F, DU B. Deep Learning for Remote Sensing Data:A Technical Tutorial on the State of the Art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):22-40. doi: 10.1109/MGRS.2016.2540798
GREENSPAN H, VAN GINNEKEN B, SUMMERS R M. Guest Editorial Deep Learning in Medical Imaging:Overview and Future Promise of an Exciting New Technique[J]. IEEE Transactions on Medical Imaging, 2016, 35(5):1153-1159. doi: 10.1109/TMI.2016.2553401
刘文举, 聂帅, 梁山, 等.基于深度学习语音分离技术的研究现状与进展[J].自动化学报, 2016, 42(6):819-833.
胡二雷, 冯瑞.基于深度学习的图像检索系统[J].计算机系统应用, 2017, 26(3):8-19.
HAMANAKA M, TANEISHI K, IWATA H, et al. CGBVS-DNN:Prediction of Compound-protein Interactions Based on Deep Learning[J]. Molecular Informatics, 2017, 36(1/2):1600045.
O'BYRNE M, PAKRASHI V, SCHOEFS F, et al. Semantic Segmentation of Underwater Imagery Using Deep Networks Trained on Synthetic Imagery[J]. Journal of Marine Science and Engineering, 2018, 6(3):93-101. doi: 10.3390/jmse6030093
BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet:A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 23(12):2481-2495.
QAWAQNEH Z, MALLOUH A A, BARKANA B D. Deep Neural Network Framework and Transformed MFCCs for Speaker's Age and Gender Classification[J]. Knowledge-Based Systems, 2017, 115:5-14. doi: 10.1016/j.knosys.2016.10.008
SHUAI L, SONG W, HONG Q, et al. Deep Variance Network:An Iterative, Improved CNN Framework for Unbalanced Training Datasets[J]. Pattern Recognition, 2018, 8(1):242-250.