Journal of Library and Information Science in Agriculture ›› 2022, Vol. 34 ›› Issue (8): 19-29.doi: 10.13998/j.cnki.issn1002-1248.22-0172
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SHI Yunlai1, CUI Yunpeng1,*, DU Zhigang2
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[1] 许丽, 焦博, 赵章瑞. 基于TF-IDF 的加权朴素贝叶斯新闻文本分类算法[J]. 网络安全技术与应用, 2021, 11: 31-33. XU L, JIAO B, ZHAO Z R.Weighted naive bayesian news text classification algorithm based on TF-IDF[J]. Network security technology & application, 2021, 11: 31-33. [2] 郭文强, 李嫔. 基于SVM的新冠疫情虚假新闻检测[J]. 佛山科学技术学院学报(自然科学版), 2021, 39(6): 19-26. GUO W Q, LI P.False news detection in the background of COVID-19 based on SVM[J]. Journal of Foshan university(natural science edition), 2021, 39(6): 19-26. [3] 田沛霖, 符海滕, 马力禹, 等. 融合对抗训练和CNN-BiGRU神经网络的新闻文本分类模型[J]. 图书情报导刊, 2021, 6(8): 38-45. TIAN P L, FU H T, MA L Y, et al.News text classification model based on adversarial training and CNN-BiGRU neural network[J]. Journal of library and information science, 2021, 6(8): 38-45. [4] 刘子昂, 蒋雪, 伍冬睿. 基于池的无监督线性回归主动学习[J]. 自动化学报, 2021, 47(12): 2771-2783. LIU Z A, JIANG X, WU D R.Unsupervised pool-based active learning for linear regression[J]. Acta automatica sinica, 2021, 47(12): 2771-2783. [5] 黄永毅, 龚垒. 基于主动学习的交互式支持向量机文本分类学习方法[J]. 电子技术与软件工程, 2016, 14(14): 168-168. HUANG Y Y, GONG L.Interactive support vector machine text classification learning method based on active learning[J]. Electronic technology & software engineering, 2016, 14(14): 168-168. [6] 邱宁佳, 丛琳, 周思丞, 等. 结合改进主动学习的 SVD-CNN 弹幕文本分类算法[J]. 计算机应用, 2019, 39(3): 644-650. QIU N J, CONG L, ZHOU S C, et al.SVD-CNN barrage text classifi-cation algorithm combined with improved active learning[J]. Journal of computer applications, 2019, 39(3): 644-650. [7] 张智雄, 刘欢, 于改红. 构建基于科技文献知识的人工智能引擎[J]. 农业图书情报学报, 2021, 33(1): 17-31. ZHANG Z X, LIU H, YU G H.Building an artificial intelligence engine based on scientific and technological literature knowledge[J]. Journal of library and information science in agriculture, 2021, 33(1): 17-31. [8] SENER O, SAVARESE S.Active learning for convolutional neural networks: A core-set approach[J]. Stat, 2018, 1050(2): 21. [9] GAL Y, GHAHRAMANI Z.Dropout as a bayesian approximation: Representing model uncertainty in deep learning[C]. International conference on machine learning, 2016: 1050-1059. [10] 杨承文, 李吉明, 杨东勇. 基于深度贝叶斯主动学习的高光谱图像分类[J]. 计算机工程与应用, 2019, 55(18): 166-172. YANG C W, LI J M, YANG D Y.Active learning for hyperspectral image classification with deep bayesian[J]. Computer engineering and applications, 2019, 55(18): 166-172. [11] DOR L E, HALFON A, GERA A, et al.Active learning for BERT: An empirical study[C]. Proceedings of the 2020 conference on empirical methods in natural language processing(EMNLP), 2020: 7949-7962. [12] HONEY J, LYNCH C D, BURKE F, et al.Ready for practice? A study of confidence levels of final year dental students at Cardiff university and university college cork[J]. European journal of dental education, 2011, 15(2): 98-103. [13] BELUCH W H, GENEWEIN T, NüRNBERGER A, et al. The power of ensembles for active learning in image classification[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 2018: 9368-9377. [14] 李涛, 郭渊博, 琚安康. 融合对抗主动学习的网络安全知识三元组抽取[J]. 通信学报, 2020, 41(10): 80-91. LI T, GUO Y B, JU A K.Knowledge triple extraction in cybersecu-rity with adversarial active learning[J]. Journal on communications, 2020, 41(10): 80-91. [15] 徐睿, 梁循, 齐金山, 等. 极限学习机前沿进展与趋势[J]. 计算机学报, 2019, 42(7): 1640-1670. XU R, LIANG X, QI J S, et al.Advances and trends in extreme learning machine[J]. Chinese journal of computers, 2019, 42(7): 1640-1670. [16] BIAU G, SCORNET E.A random forest guided tour[J]. Test, 2016, 25(2): 197-227. [17] RISH I.An empirical study of the naive bayes classifier[C]. IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001: 41-46. [18] 赵春晖, 高冰, 赵晨. 基于支持向量机和逻辑回归的半监督空谱加权的高光谱图像分类[J]. 黑龙江大学工程学报, 2019, 10(4): 64-72. ZHAO C H, GAO B, ZHAO C.Semi-supervised spectral-spatial weighted classification of hyperspectral image based on SVMSLR framework[J]. Journal of Heilongjiang hydraulic engineering college, 2019, 10(4): 64-72. [19] FRIEDMAN J H.Greedy function approximation: A gradient boosting machine[J]. Annals of statistics, 2001, 29(5): 1189-1232. [20] NOBLE W S.What is a support vector machine?[J]. Nature biotechnology, 2006, 24(12): 1565-1567. [21] RAMOS J.Using TF-IDF to determine word relevance in document queries[C]. Proceedings of the first instructional conference on machine learning, 2003: 29-48. [22] HAN K, XIAO A, WU E, et al.Transformer in transformer[J]. Advances in neural information processing systems, 2021, 34(2): 15908-15919. [23] 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, 39(12): 2481-2495. [24] 鞠默然, 罗江宁, 王仲博, 等. 融合注意力机制的多尺度目标检测算法[J]. 光学学报, 2020, 40(13): 1315002. JU M R, LUO J N, WANG Z B, et al.Multi-scale target detection algorithm based on attention mechanism[J]. Acta optica sinica, 2020, 40(13): 1315002. [25] PRECHELT L.Early stopping - But when?[J]. Neural networks: Tricks of the trade: Springer, 1998(1524): 55-69. [26] REN P, XIAO Y, CHANG X, et al.A survey of deep active learning[J]. ACM computing surveys(CSUR), 2021, 54(9): 1-40. [27] XIAO T, CAO F, LI T, et al.KNN and re-ranking models for English patent mining at NTCIR-7[C]. NTCIR, 2008. [28] ALBERT-WEISS D, OSMAN A.Interactive deep learning for shelf life prediction of muskmelons based on an active learning approach[J]. Sensors, 2022, 22(2): 414-422. [29] 金瑛, 叶飒, 李洪磊. 基于ResNet-50深度卷积网络的果树病害智能诊断模型研究[J]. 农业图书情报学报, 2021, 33(4): 58-67. JIN Y, YE S, LI H L.The intelligent diagnosis model of fruit tree disease based on ResNet-50[J]. Journal of library and information science in agriculture, 2021, 33(4): 58-67. |
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