机构

• 数学与系统科学研究院 [2]
• 自动化研究所 [2]
• 软件研究所 [2]
• 地质与地球物理研究所 [1]
• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]
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采集方式

• OAI收割 [11]

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• 期刊论文 [8]
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• 2021 [1]
• 2020 [3]
• 2018 [1]
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• Computer S... [1]
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筛选

浏览/检索结果: 共11条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Identifying Hub Wastewater Propagation Chains in China's National Economic System: A Model Coupled Input-Output Analysis with Graphical Theory 期刊论文  OAI收割 WATER, 2021, 卷号: 13, 期号: 17, 页码: 15 作者:  Li, Xuefeng;  Liu, Xiuli   |  收藏  |  浏览/下载：22/0  |  提交时间：2022/04/02 wastewater reduction  hub wastewater propagation chains  average propagation lengths-hub covariance graph (APL-HCG) model  input-occupancy-output analysis   PolSAR Image Feature Extraction via Co-Regularized Graph Embedding 期刊论文  OAI收割 REMOTE SENSING, 2020, 卷号: 12, 期号: 11, 页码: 19 作者:  Huang, Xiayuan;  Nie, Xiangli;  Qiao, Hong   |  收藏  |  浏览/下载：38/0  |  提交时间：2020/08/03 Wishart distance  Euclidean distance of polarimetric features  co-regularized graph embedding  dimensionality reduction  PolSAR image feature extraction   An efficient local search algorithm for solving maximum edge weight clique problem in large graphs 期刊论文  OAI收割 JOURNAL OF COMBINATORIAL OPTIMIZATION, 2020, 页码: 22 作者:  Chu, Yi;  Liu, Boxiao;  Cai, Shaowei;  Luo, Chuan;  You, Haihang   |  收藏  |  浏览/下载：34/0  |  提交时间：2020/12/10 Graph reduction  Maximum edge weight clique problem  Stochastic local search   A generalized least-squares approach regularized with graph embedding for dimensionality reduction 期刊论文  OAI收割 PATTERN RECOGNITION, 2020, 卷号: 98, 页码: 10 作者:  Shen, Xiang-Jun;  Liu, Si-Xing;  Bao, Bing-Kun;  Pan, Chun-Hong;  Zha, Zheng-Jun   |  收藏  |  浏览/下载：46/0  |  提交时间：2020/03/30 Dimensionality reduction  Graph embedding  Subspace learning  Least-squares   Advances and prospects of big data and mathematical geoscience 期刊论文  OAI收割 ACTA PETROLOGICA SINICA, 2018, 卷号: 34, 期号: 2, 页码: 255-263 作者:  Zhou YongZhang;  Chen Shuo;  Zhang Qi;  Xiao Fan;  Wang ShuGong   |  收藏  |  浏览/下载：71/0  |  提交时间：2018/09/29 Big Data Mining  Dimensionality Reduction  Graph Data Processing  Infinite Data Stream  Machine Learning   Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction 期刊论文  OAI收割 MATHEMATICAL PROGRAMMING, 2017, 卷号: 164, 期号: 1-2, 页码: 341-381 作者:  Ding, Chao;  Qi, Hou-Duo   |  收藏  |  浏览/下载：37/0  |  提交时间：2018/07/30 Euclidean distance matrix  Convex matrix optimization  Multidimensional scaling  Nonlinear dimensionality reduction  Low-rank matrix  Error bounds  Random graph models   Ranking Graph Embedding for Learning to Rerank 期刊论文  OAI收割 ieee transactions on neural networks and learning systems, 2013, 卷号: 24, 期号: 8, 页码: 1292-1303 作者:  Pang, Yanwei;  Ji, Zhong;  Jing, Peiguang;  Li, Xuelong 收藏  |  浏览/下载：67/0  |  提交时间：2015/06/03 Dimensionality reduction  graph embedding  image search reranking  learning to rank   An Improved KM Algorithm for Computing Structural Index of DAE System 会议论文  OAI收割 12th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), London, ENGLAND, SEP 02-04, 2012 Zeng, Yan; Wu, Xuesong; Cao, Jianwen   |  收藏  |  浏览/下载：20/0  |  提交时间：2014/12/16 DAE  Index Reduction  Structural Index  Bipartite Graph  Greedy  KM Algorithm   a problem reduction based approach to discrete optimization algorithm design 期刊论文  OAI收割 COMPUTING, 2010, 卷号: 88, 期号: 40545, 页码: 31-54 Zheng Yujun; Xue Jinyun   |  收藏  |  浏览/下载：13/0  |  提交时间：2011/05/23 Discrete optimization  Program calculation  Problem reduction graph (PRG)  Algorithm design   A method of aircraft image target recognition based on modified PCA features and SVM (EI CONFERENCE) 会议论文  OAI收割 9th International Conference on Electronic Measurement and Instruments, ICEMI 2009, August 16, 2009 - August 19, 2009, Beijing, China Donghe W.; Xin H.; Wei Z.; Huilong Y. 收藏  |  浏览/下载：29/0  |  提交时间：2013/03/25 Automatic target recognition(ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Dimensionality reduction and Classifier. After pretreatment on original features a self-organizing neural network trained with the Hebbian rule is used to extract the principal component features. Then a classifier based on Directed Acyclic Graph Support Vector Machines(DAGSVM) is adopted to recognize more than two types of aircraft targets. The experiment results show the proposed method achieves better subset features and higher recognition rate. 2009 IEEE.