| 1 | 李文俊, 杨学强.  装备保障信息系统集成研究现状[J]. 军事运筹与系统工程, 2018, 32 (2): 55- 61. | 
																													
																						|  | LI W J ,  YANG X Q .  A review of equipment support information system integration[J]. Military Operations Research and Systems Engineering, 2018, 32 (2): 55- 61. | 
																													
																						| 2 | 连云峰, 卢昱, 陈立云, 等.  军械装备保障模拟训练系统数据集成研究[J]. 信息技术, 2011, 5, 8- 11. | 
																													
																						|  | LIAN Y F ,  LU Y ,  CHEN L Y , et al.  Research on data integration of ordnance equipment training simulation system[J]. Information Technology, 2011, 5, 8- 11. | 
																													
																						| 3 | 金星, 李江杰, 桑运海.  装备保障数据规划与集成应用研究[J]. 航空科学技术, 2014, 7, 48- 54. | 
																													
																						|  | JIN X ,  LI J J ,  SANG Y H .  Study on data planning and integration of equipment support system[J]. Aeronautical Science & Technology, 2014, 7, 48- 54. | 
																													
																						| 4 | 周钢, 郭福亮.  面向服务的军械装备保障业务数据集成架构设计[J]. 计算机与现代化, 2017, 3, 112- 116. | 
																													
																						|  | ZHOU G ,  GUO F L .  Data integration architecture design for service-oriented ordnance equipment support service[J]. Computer and Modernization, 2017, 3, 112- 116. | 
																													
																						| 5 | 张惠民, 胡海荣, 崔伟宁, 等.  基于SOA数据共享技术在装备保障系统中的应用[J]. 四川兵工学报, 2013, 34 (7): 65- 66. | 
																													
																						|  | ZHANG H M ,  HU H R ,  CUI W N , et al.  Study and use of data synchronization in the general equipment support information system based on SOA[J]. Journal of Sichuan Ordnance, 2013, 34 (7): 65- 66. | 
																													
																						| 6 | LABANI M ,  MORADI P ,  AHMADIZAR F , et al.  A novel multivariate filter method for feature selection in text classification problems[J]. Engineering Applications of Artificial Intelligence, 2018, 70, 25- 37. doi: 10.1016/j.engappai.2017.12.014
 | 
																													
																						| 7 | KOWSARI K, BROWN D E, HEIDARYSAFA M, et al. HDLTex: hierarchical deep learning for text classification[C]//Proc.of the International Conference on Machine Learning and Application, 2017: 364-371. | 
																													
																						| 8 | PAVLINEK M ,  PODGORELEC V .  Text classification method based on self-training and LDA topic models[J]. Expert Systems with Applications, 2017, 80, 83- 93. doi: 10.1016/j.eswa.2017.03.020
 | 
																													
																						| 9 | SOPHIE B ,  STEFAN K .  Online multi-label dependency topic models for text classification[J]. Machine Learning, 2018, 107 (5): 859- 886. doi: 10.1007/s10994-017-5689-6
 | 
																													
																						| 10 | 朱永利, 李莉, 宋亚奇, 等.  ODPS平台下的电力设备监测大数据存储与并行处理方法[J]. 电工技术学报, 2017, 32 (9): 199- 210. | 
																													
																						|  | ZHU Y L ,  LI L ,  SONG Y Q , et al.  Storage and parallel processing of big data of power equipment condition monitoring on ODPS platform[J]. Transactions of China Electrotechnical Society, 2017, 32 (9): 199- 210. | 
																													
																						| 11 | PURIPUNPINYO H, SAMADZADEH M H. Design, prototype implementation, and comparison of scalable web-push architectures on amazon web services using the actor model[C]//Proc.of the 25th International Conference on Systems Engineering, 2017: 301-308. | 
																													
																						| 12 | CHINTAPALLI S, DAGIT D, EVANS B, et al. Benchmarking streaming computation engines: storm, flink and spark streaming[C]// Proc.of the International Parallel and Distributed Processing Symposium Workshops, 2016: 1789-1792. | 
																													
																						| 13 | 侯一鸣, 徐泉, 李亚杰, 等.  基于物联网和工业云的选矿设备状态监控系统[J]. 计算机集成制造系统, 2017, 23 (9): 1972- 1981. | 
																													
																						|  | HOU Y M ,  XU Q ,  LI Y J , et al.  Monitoring system for mine-ral processing equipment based on IoT and industrial cloud computing[J]. Computer Integrated Manufacturing Systems, 2017, 23 (9): 1972- 1981. | 
																													
																						| 14 | WANG T ,  CHEN M J ,  ZHAO H Y , et al.  Estimating a sparse reduction for general regression in high dimensions[J]. Statistics and Computing, 2018, 28 (1): 33- 46. doi: 10.1007/s11222-016-9714-6
 | 
																													
																						| 15 | KIM S H ,  LEE N ,  EBSTYNE P K .  Dimensions of religion and spirituality: a longitudinal topic modeling approach[J]. Journal for the Scientific Study of Religion, 2020, 59 (1): 62- 83. doi: 10.1111/jssr.12639
 | 
																													
																						| 16 | YU Y, SILVEIRA H, SUNDARAM M. A microservice based reference architecture model in the context of enterprise architecture[C]//Proc.of the Advanced Information Management, Communicates, Electronic and Automation Control Confe-rence, 2016: 1856-1860. | 
																													
																						| 17 | KARAMTI H ,  TMAR M ,  VISANI M , et al.  Vector space model adaptation and pseudo relevance feedback for content-based image retrieval[J]. Multimedia Tools and Applications, 2018, 77 (5): 5475- 5501. doi: 10.1007/s11042-017-4463-x
 | 
																													
																						| 18 | 华珍.文本聚类中特征选择方法研究[D].武汉:湖北工业大学, 2016. | 
																													
																						|  | HUA Z. Study on feature selection method of text clustering[D]. Wuhan: Hubei University of Technology, 2016. | 
																													
																						| 19 | SANTHANAKUMAR M ,  CHRISTOPHER C C ,  JAYAPRIYA K .  Multi term based co-term frequency method for term weighting in information retrieval[J]. International Journal of Business Information Systems, 2018, 28 (1): 79- 94. doi: 10.1504/IJBIS.2018.091164
 | 
																													
																						| 20 | WANG Y W ,  FENG L Z ,  ZHU J M .  Novel artificial bee colony based feature selection method for filtering redundant information[J]. Applied Intelligence, 2018, 48 (4): 868- 885. doi: 10.1007/s10489-017-1010-4
 | 
																													
																						| 21 | CHEN J N ,  HUANG H K .  Feature selection for text classification with naive Bayes[J]. Pattern Recognition Letters, 2015, 36 (3): 5432- 5435. | 
																													
																						| 22 | KO Y .  How to use negative class information for naive Bayes classification[J]. Information Processing & Management, 2017, 53 (6): 1255- 1268. | 
																													
																						| 23 | TAEHO J. K nearest neighbor for text summarization using feature similarity[C]//Proc.of the International Conference on Communication, Control, Computing and Electronics Enginee-ring, 2017.DOI: 10.1109/ICCCCEE.2017.7866705. | 
																													
																						| 24 | KUMAR P S .  Similar vague concepts selection using their euclidean distance at different granulation[J]. Cognitive Computation, 2018, 10 (2): 228- 241. doi: 10.1007/s12559-017-9527-8
 | 
																													
																						| 25 | SAHELI M ,  KHAJEPOUR S G .  Fuzzy inner product spaces[J]. Fuzzy Sets and Systems, 2016, 303 (2): 149- 162. | 
																													
																						| 26 | LI B, HAN L. Distance weighted cosine similarity measure for text classification[C]//Proc.of the 14th International Confe-rence on Intelligent Data Engineering and Automated Learning, 2013: 611-618. | 
																													
																						| 27 | YANG X, JIN P, CHEN X Y. The construction of a kind of chat corpus in Chinese word segmentation[C]//Proc.of the International Conference on Web Intelligence and Intelligent Agent Technology, 2015: 168-172. | 
																													
																						| 28 | SOKOLOVA M .  Big text advantages and challenges: classification perspective[J]. International Journal of Data Science and Analytics, 2018, 5 (1): 1- 10. |