Agricultural Library and Information ›› 2019, Vol. 31 ›› Issue (10): 12-22.doi: 10.13998/j.cnki.issn1002-1248.2019.09.19-0652
• Special review • Previous Articles Next Articles
HU Lin1,2, LIU Tingting1, LI Huan1,2, CUI Yunpeng1,2
CLC Number:
| [1] 陈春霞. 浅析机器学习的发展与应用[J]. 信息系统工程, 2017(8):99-100. [2] Yildiz B, Bilbao J I, Sproul A B.A review and analysis of regression and machine learning models on commercial building electricity load forecasting[J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 73: 1104-1122. [3] Jordan M I, Mitchell T M.Machine learning: Trends, perspectives, and prospects[J]. SCIENCE, 2015, 349(6245, SI): 255-260. [4] 焦嘉烽, 李云. 大数据下的典型机器学习平台综述[J]. 计算机应用, 2017(11):7-15+20. [5] 余殷博. 基于人工智能下的机器学习历史及展望[J]. 电子技术与软件工程, 2017(4):129-129. [6] 崔运鹏,王健,刘娟. 基于深度学习的自然语言处理技术的发展及其在农业领域的应用[J]. 农业大数据学报,2019,1(1):38-44. [7] Wayne Thompson, Hui Li and Alison Bolen. Artificial intelligence, machine learning, deep learning and beyond Understanding AI technologies and how they lead to smart applications[J/OL].[2019-03-12].https://www.sas.com/zh_cn/insights/articles/big-data/artificial-intelligence-machine-learning-deep-learning-and-beyond.html. [8] MathWorks. 什么是机器学习?您需要知道的三件事[EB/OL]. [2019-02-01].https://ww2.mathworks.cn/discovery/machine-learning.html?s_tid=srchtitle. [9] 陈嘉博. 机器学习算法研究及前景展望[J]. 信息通信, 2017(6). [10] 夏天. 机器学习及其算法与应用研究[J]. 电脑知识与技术, 2017, 13(15):156-157. [11] 张素芳, 翟俊海, 王聪,等. 大数据与大数据机器学习[J]. 河北大学学报(自然科学版), 2018, v.38(3):81-90+118. [12] 师翊,耿楠,胡少军,张志毅,张晶.基于随机森林回归算法的苹果树冠层光照分布模型[J].农业机械学报,2019,50(05):214-222. [13] 官赛萍,靳小龙,贾岩涛,王元卓,程学旗.面向知识图谱的知识推理研究进展[J].软件学报,2018,29(10):2966-2994. [14] 张海波.基于正则化卷积神经网络的目标跟踪算法[J].信息技术,2019,43(06):82-86+90. [15] 汤鲲, 蒋炳南, 彭艳兵. 基于决策树的多维属性自动推理识别[J]. 计算机与现代化, 2017(2):83-87. [16] 李俊明. 中国PM_(2.5)污染时空趋势研究——基于贝叶斯时空统计视角[J].统计与信息论坛,2019,34(06):67-73. [17] 章永来,周耀鉴.聚类算法综述[J/OL].计算机应用:1-14.[2019-07-11].http://kns.cnki.net/kcms/detail/51.1307.TP.20190415.1412.004.html. [18] 李家辉,周忠眉.关联分类及其改进算法综述[J].科技通报,2018,34(08):140-144. [19] 刘俊一. 基于人工神经网络的深度学习算法综述[J].中国新通信,2018,20(06):193-194. [20] 李明旭. 基于人工神经网络的秀丽隐杆线虫趋温性行为建模[D].重庆邮电大学,2017. [21] 王婷,崔运鹏,王健,刘婷婷,王末.认知计算及其在农业领域的应用研究[J].农业图书情报,2019,31(4):4-18. [22] 鄂海红,张文静,肖思琪,程瑞,胡莺夕,周筱松,牛佩晴.深度学习实体关系抽取研究综述[J].软件学报,2019,30(06):1793-1818. [23] 范玉涛. 空间数据挖掘中的降维算法研究[D].辽宁师范大学,2015. [24] 张恩豪,陈晓红,刘鸿,朱玉莲.基于典型相关分析的多视图降维算法综述[J/OL].计算机工程:1-15.[2019-07-11].https://doi.org/10.19678/j.issn.1000-3428.0053147. [25] 林思寒,黎静,薛龙,刘木华,陈金印,陈明,张一帆.基于时序高光谱的翠冠梨机械损伤的早期无损检测研究[J].江西农业大学学报,2018,40(04):835-842. [26] 徐继伟,杨云.集成学习方法:研究综述[J].云南大学学报(自然科学版),2018,40(06):1082-1092. [27] 王奕森,夏树涛.集成学习之随机森林算法综述[J].信息通信技术,2018,12(01):49-55. [28] 于玲,吴铁军.集成学习:Boosting算法综述[J].模式识别与人工智能,2004,17(01):52-59. [29] 蔡毅,朱秀芳,孙章丽,陈阿娇.半监督集成学习综述[J].计算机科学,2017,44(S1):7-13. [30] 任涵. 基于遗传算法的猕猴桃组培苗变异监测研究[D].昆明理工大学,2017. [31] Kumar M, Husian M, Upreti N, et al.Genetic algorithm: Review and application[J]. International Journal of Information Technology and Knowledge Management, 2010, 2(2): 451-454. [32] 刘方园,王水花,张煜东.支持向量机模型与应用综述[J].计算机系统应用,2018,27(04):1-9. [33] Mountrakis G, Im J, Ogole C.Support vector machines in remote sensing: A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(3): 247-259. [34] Scholkopf B, Smola A J.Learning with kernels: support vector machines, regularization, optimization, and beyond[M]. MIT press, 2001. [35] 赵彰. 机器学习研究范式的哲学基础及其可解释性问题[D].上海社会科学院,2018. [36] He D, Xia Y, Qin T, et al.Dual learning for machine translation[C]//Advances in Neural Information Processing Systems. 2016: 820-828. [37] 夏应策. 对偶学习的理论和实验研究[D].中国科学技术大学,2018. [38] 吴宏杰,戴大东,傅启明,陈建平,陆卫忠.强化学习与生成式对抗网络结合方法研究进展[J].计算机工程与应用,2019,55(10):36-44. [39] 邢恩旭,吴小勇,李雅娴.基于迁移学习的双层生成式对抗网络[J/OL].计算机工程与应用:1-11.[2019-07-11].http://kns.cnki.net/kcms/detail/11.2127.TP.20190308.0942.008.html. [40] Bzdok D, Altman N, Krzywinski M.Points of significance: statistics versus machine learning[J]. 2018. [41] Gu W, Foster K, Shang J, et al.A game-predicting expert system using big data and machine learning[J]. Expert Systems with Applications, 2019, 130: 293-305. [42] Holzinger A, Kieseberg P, Weippl E, et al.Current advances, trends and challenges of machine learning and knowledge extraction: From machine learning to explainable ai[C]//International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer, Cham, 2018: 1-8. [43] Ahmad I, Basheri M, Iqbal M J, et al.Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection[J]. IEEE Access, 2018, 6: 33789-33795. [44] González-Camacho J M, Ornella L, Pérez-Rodríguez P, et al. Applications of machine learning methods to genomic selection in breeding wheat for rust resistance[J]. The plant genome, 2018. [45] Li B, Zhang N, Wang Y G, et al.Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods[J]. Frontiers in genetics, 2018, 9: 237. [46] Odilbekov F, Armoniené R, Henriksson T, et al.Proximal phenotyping and machine learning methods to identify septoria tritici blotch disease symptoms in wheat[J]. Frontiers in plant science, 2018, 9: 685. [47] Han X, Huettmann F, Guo Y, et al.Conservation prioritization with machine learning predictions for the black-necked crane Grus nigricollis, a flagship species on the Tibetan Plateau for 2070[J]. Regional environmental change, 2018, 18(7): 2173-2182. [48] Qiu Z, Cheng Q, Song J, et al.Application of machine learning-based classification to genomic selection and performance improvement[C] //International Conference on Intelligent Computing. Springer, Cham, 2016: 412-421. [49] Mohanty S P, Hughes D P, Salathé M.Using deep learning for image-based plant disease detection[J]. Frontiers in plant science, 2016, 7: 1419. [50] Fuentes A, Yoon S, Kim S, et al.A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition[J]. Sensors, 2017, 17(9): 2022. [51] Corrales D C, Corrales J C, Figueroa-Casas A.Towards detecting crop diseases and pest by supervised learning[J]. Ingeniería y Universidad, 2015, 19(1): 207-228. [52] Hepworth P J, Nefedov A V., Muchnik I B.Broiler chickens can benefit from machine learning: Support vector machine analysis of observational epidemiological data[J]. Journal of the Royal Society Interface, 2012(9):1934-1942. [53] Liakos K, Busato P, Moshou D, et al.Machine learning in agriculture: A review[J]. Sensors, 2018, 18(8): 2674. [54] Schuster E W, Kumar S, Sarma S E, et al.Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques[C]//2011 8th International Conference & Expo on Emerging Technologies for a Smarter World. IEEE, 2011: 1-6. [55] Dahikar S S, Rode S V.Agricultural crop yield prediction using artificial neural network approach[J]. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2014, 2(1): 683-686. [56] Chlingaryan A, Sukkarieh S, Whelan B.Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review[J].Computers and electronics in agriculture, 2018, 151: 61-69. [57] Sahoo S, Russo T A, Elliott J, et al.Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US[J]. Water Resources Research, 2017, 53(5): 3878-3895. [58] Bendre M R, Thool R C, Thool V R.Big data in precision agriculture: Weather forecasting for future farming[C]//2015 1st International Conference on Next Generation Computing Technologies (NGCT). IEEE, 2015: 744-750. [59] Kussul N, Lavreniuk M, Skakun S, et al.Deep learning classification of land cover and crop types using remote sensing data[J]. IEEEGeoscience and Remote Sensing Letters, 2017, 14(5): 778-782. [60] Zhong L, Hu L, Zhou H.Deep learning based multi-temporal crop classification[J]. Remote sensing of environment, 2019, 221: 430-443. [61] Park S, Im J, Jang E, et al.Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions[J]. Agricultural and forest meteorology, 2016, 216: 157-169. [62] Park S, Im J, Park S, et al.AMSR2 soil moisture downscaling using multisensor products through machine learning approach[C]//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2015: 1984-1987. [63] De Vito S, Esposito E, Salvato M, et al.Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches[J]. Sensors and Actuators B: Chemical, 2018, 255: 1191-1210. [64] Wang J, Xie J, Zhao R, et al.A new probabilistic kernel factor analysis for multisensory data fusion: application to tool condition monitoring[J].IEEE Transactions on Instrumentation and Measurement, 2016, 65(11): 2527-2537. [65] 裘炯良, 周力沛, 郑剑宁等. 机器学习技术与病媒生物种属鉴定[J]. 中华卫生杀虫药械, 2017,(5):436-439. [66] 孙存一, 龚六堂. 大数据思维下的利率定价研究——以机器学习为视角的实证分析[J]. 金融理论与实践, 2017(7):1-5. [67] 孙美卫. 一种基于机器学习的经济数据识别方法[J]. 佳木斯大学学报(自然科学版), 2018,36(3):137-140. [68] Milacic L, Jovic S, Vujovic T, et al.Application of artificial neural network with extreme learning machine for economic growth estimation[J]. Physica A: Statistical Mechanics and its Applications, 2017, 465: 285-288. [69] Malhotra A,Maloo M.了解印度的食品通货膨胀:一种机器学习方法[J].arXiv:机器学习,2017. [70] Jean N, Burke M, Xie M, et al.Combining satellite imagery and machine learning to predict poverty[J]. Science, 2016, 353(6301): 790-794. [71] Wang M, Cui Y, Wang X, et al.Machine learning for networking: Workflow, advances and opportunities[J]. IEEE Network, 2017, 32(2): 92-99. [72] Qin S J, Chiang L H.Advances and opportunities in machine learning for process data analytics[J]. Computers & Chemical Engineering, 2019, 126: 465-473. [73] Baydin A G, Pearlmutter B A, Radul A A, et al.Automatic differentiation in machine learning: a survey[J]. Journal of machine learning research, 2018, 18(153). |
|
||
