开课单位--马里兰大学
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Tropical Cyclone Event Sequence Similarity Search via Dimensionality Reduction and Metric Learning[热带气旋事件序列相似性的维数约简和度量学习研究 ]
  Shen-Shyang Ho(马里兰大学) The Earth Observing System Data and Information System (EOSDIS) is a comprehensive data and information system which archives, manages, and distribute...
热度:71

32
Mining Rich Session Context to Improve Web Search[挖掘富会话上下文以改进网页搜索]
  Guangyu Zhu(马里兰大学) User browsing information, particularly their non-search related activity, reveals important contextual information on the preferences and the intent ...
热度:43

33
Anomalous Window Discovery through Scan Statistics for Linear Intersecting Paths (SSLIP)[线性相交路径扫描统计异常窗口发现(SSLIP)]
  Lei Shi(马里兰大学) Anomalous windows are the contiguous groupings of data points. In this paper, we propose an approach for discovering anomalous windows using Scan Stat...
热度:38

34
Link prediction for annotation graph datasets using graph summarization[使用图表汇总链接预测注释图表数据集]
  Andreas Thor(马里兰大学) Annotation graph datasets are a natural representation of scientifi c knowledge. They are common in the life sciences where genes or proteins are anno...
热度:54

35
RDF123: from Spreadsheets to RDF[RDF123:从电子表格到RDF]
  Lushan Han(马里兰大学) We describe RDF123, a highly flexible open-source tool for translating spreadsheet data to RDF. Existing spreadsheet-to-rdf tools typically map only t...
热度:73

36
Differential Adaptive Diffusion: Understanding Diversity and Learning Whom to Trust in Viral Marketing[差异适应扩散:理解多样性,学习信任病毒营销的人]
  Hossam Sharara(马里兰大学 ) Viral marketing mechanisms use the existing social network between customers to spread information about products and encourage product adoption. Exis...
热度:31

37
Supervised Learning from Multiple Experts: Whom to Trust When Everyone Lies a Bit [多专家监督学习:每个人都信任谁]
  Vikas Raykar(马里兰大学) We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolut...
热度:33

38
Probabilistic Dyadic Data Analysis with Local and Global Consistency[具有局部和全局一致性的概率二元数据分析]
  Vikas Raykar(马里兰大学) Dyadic data arises in many real world applications such as social network analysis and information retrieval. In order to discover the underlying or h...
热度:49

39
Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer[贝叶斯多实例学习:自动特征选择和归纳传递]
  Vikas Raykar(马里兰大学) We propose a novel Bayesian multiple instance learning algorithm. This algorithm automatically identifies the relevant feature subset, and utilizes in...
热度:68
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