图 书 馆开课单位--伊利诺伊大学
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1PReP: PathBased Relevance from a Probabilistic Perspective in Heterogeneous Information Networks[PReP：异构信息网络中概率视角的基于路径的相关性]
于石(伊利诺伊大学) As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defini...
热度：24
于石(伊利诺伊大学) As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defini...
热度：24
2TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams[TrioVecEvent：地理标记推文流中基于嵌入的在线本地事件检测]
Chao Zhang(伊利诺伊大学) Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster contr...
热度：20
Chao Zhang(伊利诺伊大学) Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster contr...
热度：20
3Learning without Forgetting[学而不忘]
Zhizhong Li(伊利诺伊大学) When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is al...
热度：25
Zhizhong Li(伊利诺伊大学) When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is al...
热度：25
4MATCH: Metadata-Aware Text Classification in A Large Hierarchy[MATCH：大型层次结构中的元数据感知文本分类]
Yu Zhang(伊利诺伊大学) Metadata-Aware Text Classification in A Large Hierarchy
热度：28
Yu Zhang(伊利诺伊大学) Metadata-Aware Text Classification in A Large Hierarchy
热度：28
5TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering[TaxonGen：基于自适应术语嵌入和聚类的无监督主题分类构建]
Chao Zhang(伊利诺伊大学) Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as inform...
热度：29
Chao Zhang(伊利诺伊大学) Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as inform...
热度：29
6Mining Topics in Documents: Standing on the Shoulders of Big Data[文档主题挖掘:站在大数据的肩膀上]
Zhiyuan (Brett) Chen(伊利诺伊大学) Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (...
热度：23
Zhiyuan (Brett) Chen(伊利诺伊大学) Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (...
热度：23
7Classification Approach Towards Ranking and Sorting[分类方法对排序和排序]
Shyamsundar Rajaram(伊利诺伊大学) Classification Approach Towards Ranking and Sorting
热度：39
Shyamsundar Rajaram(伊利诺伊大学) Classification Approach Towards Ranking and Sorting
热度：39
8Semidefinite ranking on graphs[图的半定排序]
Shankar Vembu(伊利诺伊大学) We consider the problem of ranking the vertices of an undirected graph given some preference relation. This ranking on graphs problem has been tackled...
热度：28
Shankar Vembu(伊利诺伊大学) We consider the problem of ranking the vertices of an undirected graph given some preference relation. This ranking on graphs problem has been tackled...
热度：28
9Diagnosing Error in Object Detectors[目标探测器的诊断误差]
Stefan Carlsson;Antonio Torralba;Derek Hoiem(伊利诺伊大学) This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of f...
热度：123
Stefan Carlsson;Antonio Torralba;Derek Hoiem(伊利诺伊大学) This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of f...
热度：123
10Mining Heterogeneous Information Networks[挖掘异构信息网络]
Jiawei Han(伊利诺伊大学) Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information ne...
热度：45
Jiawei Han(伊利诺伊大学) Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information ne...
热度：45