机构

• 自动化研究所 [3]
• 地理科学与资源研究所 [1]
• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]
• 科技战略咨询研究院 [1]
• 沈阳自动化研究所 [1]
• 更多

采集方式

• OAI收割 [8]

内容类型

• 期刊论文 [7]
• 会议论文 [1]

发表日期

• 2023 [1]
• 2022 [2]
• 2021 [1]
• 2020 [1]
• 2018 [1]
• 2015 [1]
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学科主题

筛选

浏览/检索结果: 共8条，第1-8条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择作者升序作者降序发表日期升序发表日期降序题名升序题名降序提交时间升序提交时间降序 Maximizing the Spread of Effective Information in Social Networks 期刊论文  OAI收割 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 卷号: 35, 期号: 4, 页码: 4062-4076 作者:  Zhang, Haonan;  Fu, Luoyi;  Ding, Jiaxin;  Tang, Feilong;  Xiao, Yao   |  收藏  |  浏览/下载：36/0  |  提交时间：2024/01/04 Social networking (online)  Mouth  Smart phones  Estimation  Approximation algorithms  Time complexity  Statistics  Social network  influence maximization  information variation  greedy algorithm   Explanation guided cross-modal social image clustering 期刊论文  OAI收割 INFORMATION SCIENCES, 2022, 卷号: 593, 页码: 1-16 作者:  Yan, Xiaoqiang;  Mao, Yiqiao;  Ye, Yangdong;  Yu, Hui;  Wang, Fei-Yue   |  收藏  |  浏览/下载：61/0  |  提交时间：2022/06/10 Social image clustering  Human explanation  Side information  Information maximization  Interactive optimization   MAHE-IM: Multiple Aggregation of Heterogeneous Relation Embedding for Influence Maximization on Heterogeneous Information Network 期刊论文  OAI收割 EXPERT SYSTEMS WITH APPLICATIONS, 2022, 卷号: 202 作者:  Li, Ying;  Li, Linlin;  Liu, Yijun;  Li, Qianqian   |  收藏  |  浏览/下载：33/0  |  提交时间：2023/05/30 Influence maximization  Heterogeneous information network  Network embedding   Learning diffusion model-free and efficient influence function for influence maximization from information cascades 期刊论文  OAI收割 KNOWLEDGE AND INFORMATION SYSTEMS, 2021, 页码: 24 作者:  Cao, Qi;  Shen, Huawei;  Gao, Jinhua;  Cheng, Xueqi   |  收藏  |  浏览/下载：86/0  |  提交时间：2021/12/01 Influence function learning  Influence maximization  Information cascades  Social network   Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm 期刊论文  OAI收割 IEEE ACCESS, 2020, 卷号: 8, 页码: 160285-160296 作者:  Yu SQ(余思泉);  Shen GP(沈贵萍);  Wang PY(王佩瑶);  Wang YN(王宇宁);  Wu CD(吴成东)   |  收藏  |  浏览/下载：56/0  |  提交时间：2020/10/06 Clustering algorithms  Mutual information  Image representation  Neural networks  Clustering methods  Feature extraction  Task analysis  Representation learning  image clustering  mutual information maximization  self-training algorithm   Study on Information Diffusion Analysis in Social Networks and Its Applications 期刊论文  OAI收割 International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 4, 页码: 377-401 作者:  Biao Chang;  Tong Xu, Qi Liu;  En-Hong Chen   |  收藏  |  浏览/下载：48/0  |  提交时间：2021/02/23 Information diffusion  influence evaluation  influence maximization  information source detection  social network.   Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization 期刊论文  OAI收割 IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2015, 卷号: E98D, 期号: 7, 页码: 1422-1425 作者:  Liu, Shuang;  Zhang, Zhong;  Xiao, Baihua;  Cao, Xiaozhong 收藏  |  浏览/下载：38/0  |  提交时间：2015/09/23 discriminative patterns  mutual information maximization  ground-based cloud classification   A new research of sub-pixel level accuracy of TDICCD remote sensing image registration (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China 作者:  He B. 收藏  |  浏览/下载：44/0  |  提交时间：2013/03/25 In the field of remote sensing imaging  TDICCD remote sensing images have a lot of their own characteristics  such as high-resolution  large amount of information  less overlapping parts of pixels  additional image blurring etc. Therefore  there exist many difficulties  especially in terms of high-accuracy registration of pairs of images. For that  this paper presents two new pixel interpolation method for sub-pixel level registering images that allows for scaling  translation and rotation. The proposed technique  which is based on the maximization of the correlation coefficient function  combines an efficient pixel-moving interpolation scheme with surface fitting  which greatly reduces the overall computational cost. The accuracy of the algorithm is evaluated by calculating correlation coefficient of couples of points belonging to images transformed with preset factors and also comparing it to other sorts of methods. The experiment results show that the accuracy of registration reaches 0.01 pixels. 2010 IEEE.