图 书 馆开课单位--KDD 2016研讨会
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1Parallel News-Article Traffic Forecasting with ADMM[ADMM并行新闻报道流量预测]
Stratis Ioannidis(KDD 2016研讨会) Predicting the traffic of an article, as measured by page views, is of great importance to content providers. Articles with increased traffic can impr...
热度：8
Stratis Ioannidis(KDD 2016研讨会) Predicting the traffic of an article, as measured by page views, is of great importance to content providers. Articles with increased traffic can impr...
热度：8
2On the Effect of Endpoints on Dynamic Time Warping[端点对动态时间扭曲的影响]
Diego Furtado Silva(KDD 2016研讨会) While there exist a plethora of classification algorithms for most data types, there is an increasing acceptance that the unique properties of time se...
热度：10
Diego Furtado Silva(KDD 2016研讨会) While there exist a plethora of classification algorithms for most data types, there is an increasing acceptance that the unique properties of time se...
热度：10
3New Time Series Methods for Flu Forecasting[流感预测的新时间序列方法]
Naren Ramakrishnan(KDD 2016研讨会) There has been recent concerted interest in computational methods for forecasting the flu, spurred by competitions organized by agencies like the CDC ...
热度：10
Naren Ramakrishnan(KDD 2016研讨会) There has been recent concerted interest in computational methods for forecasting the flu, spurred by competitions organized by agencies like the CDC ...
热度：10
4Short-term Time Series Forecasting with Regression Automata[基于回归自动机的短期时间序列预测]
Massimo Chenal(KDD 2016研讨会) We present regression automata (RA), which are novel type syntactic models for time series forecasting. Building on top of conventional state-merging ...
热度：7
Massimo Chenal(KDD 2016研讨会) We present regression automata (RA), which are novel type syntactic models for time series forecasting. Building on top of conventional state-merging ...
热度：7
5Scalable Clustering of Correlated Time Series using Expectation Propagation[基于期望传播的相关时间序列可扩展聚类]
Christopher Aicher(KDD 2016研讨会) We are interested in finding clusters of time series such that series within a cluster are correlated and series between clusters are independent. Exi...
热度：9
Christopher Aicher(KDD 2016研讨会) We are interested in finding clusters of time series such that series within a cluster are correlated and series between clusters are independent. Exi...
热度：9
6Sparse plus low-rank graphical models of time series for functional connectivity in MEG[MEG中函数连接的稀疏加低阶时间序列图形模型]
Rahul Nadkarni(KDD 2016研讨会) Inferring graphical models from high dimensional observations has become an important problem in machine learning and statistics because of its import...
热度：12
Rahul Nadkarni(KDD 2016研讨会) Inferring graphical models from high dimensional observations has become an important problem in machine learning and statistics because of its import...
热度：12
7Time Lag Concerned Dynamic Dependency Network Structure Learning[时滞相关动态依赖网络结构学习]
Lei Han(KDD 2016研讨会) Characterizing and understanding the structure and the evolution of networks is an important problem for many different fields. While in the real-worl...
热度：8
Lei Han(KDD 2016研讨会) Characterizing and understanding the structure and the evolution of networks is an important problem for many different fields. While in the real-worl...
热度：8
8Stream Data Mining: A Big Data Perspective[流数据挖掘：大数据视角]
Latifur Khan(KDD 2016研讨会) Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams ...
热度：7
Latifur Khan(KDD 2016研讨会) Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams ...
热度：7
9Ranking academic institutions on potential paper acceptance in upcoming conferences[对学术机构的潜在论文接受度进行排名]
Jobin Wilson(KDD 2016研讨会) Ranking academic institutions on potential paper acceptance in upcoming conferences
热度：9
Jobin Wilson(KDD 2016研讨会) Ranking academic institutions on potential paper acceptance in upcoming conferences
热度：9
10Space-Time Clustering with Stability Probe while Riding Downhill[下坡时使用稳定性探针进行时空聚类]
Xin Huang(KDD 2016研讨会) We propose a new data-driven procedure for optimal selection of tuning parameters in dynamic clustering algorithms, using the notion of stability prob...
热度：9
Xin Huang(KDD 2016研讨会) We propose a new data-driven procedure for optimal selection of tuning parameters in dynamic clustering algorithms, using the notion of stability prob...
热度：9