2015C-CIKM-Detect Rumors Using Time Series of Social Context Information on Microblogging Websites
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites（2015CIKM），JingMa 主要內容 提出一個動態時間序列結構模型（Dynamic Series-Time Structure model——DSTS），能夠抓取多種社會上下文特徵隨時間流逝的變化。實驗結果表明，在得知
相關文章
- 1. An overview of time series forecasting models
- 2. Multiple websites on single instance of IIS
- 3. Time-Series Analysis
- 4. Social Information Graph
- 5. How To Make Your Websites Faster On Mobile Devices
- 6. Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data
- 7. 閱讀筆記：Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests（Python package）
- 8. A Proposal on Organization of Information System
- 9. Scaling Time Series Data Storage — Part I
- 10. 《A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling》筆記
- 更多相關文章...
- • PHP time() 函數 - PHP參考手冊
- • XSLT
- • RxJava操作符（六）Utility
- • RxJava操作符（一）Creating Observables
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. An overview of time series forecasting models
- 2. Multiple websites on single instance of IIS
- 3. Time-Series Analysis
- 4. Social Information Graph
- 5. How To Make Your Websites Faster On Mobile Devices
- 6. Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data
- 7. 閱讀筆記：Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests（Python package）
- 8. A Proposal on Organization of Information System
- 9. Scaling Time Series Data Storage — Part I
- 10. 《A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling》筆記