开课单位--马萨诸塞大学
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11
Relational Data Pre-Processing Techniques for Improved Securities Fraud Detection[用于改进证券欺诈检测的关系数据预处理技术 ]
  Andrew Fast(马萨诸塞大学) Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over ...
热度:61

12
Theory of macromolecular transport through protein channels and nanopores[蛋白质通道和纳米孔大分子输运理论 ]
  Murugappan Muthukumar(马萨诸塞大学) An understanding of the ubiquitous phenomenon of translocation of electrically charged macromolecules through narrow channels requires an adequate des...
热度:82

13
Regularized Off-Policy TD-Learning[规范化非政策TD学习]
  Bo Liu(马萨诸塞大学) We present a novel l1 regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value func...
热度:74

14
A Mirror of Social Development: Industry decisions regarding new technologies[社会发展的镜子:关于新技术的行业决策]
  Jennifer Geertsma(马萨诸塞大学) This study examines how organizations manage risks presented by new technologies and the impact the institutional environment has on this governance p...
热度:36

15
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression[基于Dirichlet多项式回归的任意特征的主题模型]
  Andrew McCallum(马萨诸塞大学) Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations ...
热度:240

16
Finding Text Reuse on the Web[在Web上查找文本重用]
  Michael Bendersky; Bruce Croft(马萨诸塞大学)  Finding Text Reuse on the Web
热度:30

17
Human-Machine Cooperation: User Corrections for AKBC[人机合作:为akbc用户而修正]
  Michael L. Wick(马萨诸塞大学) Knowledge bases (KB) provide support for real-world decision making by exposing data in a structured format. However, constructing knowledge bases req...
热度:15

18
Query Reformulation Using Anchor Text[使用锚文本进行查询重构]
  Van Dang(马萨诸塞大学) Query reformulation techniques based on query logs have been studied as a method of capturing user intent and improving retrieval effectiveness. The e...
热度:33

19
Learning the structure of deep sparse graphical models[学习深层稀疏图形模型的结构]
  Hanna M. Wallach(马萨诸塞大学) Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief networ...
热度:25

20
MCMC Inference Inside the DB for Extraction, Resolution, Alignment, Provenance and Queries[MCMC内部提取,分辨率,对齐,出处和查询推断]
  Andrew McCallum(马萨诸塞大学) MCMC内部提取,分辨率,对齐,出处和查询推断 
热度:52
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