开课单位--俄勒冈州立大学
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Cost-Sensitive Top-Down/Bottom-Up Inference for Multiscale Activity Recognition[多尺度活动识别的成本敏感自顶向下/自下而上推理]
   Mohamed Amer(俄勒冈州立大学) This paper addresses a new problem, that of multiscale activity recognition. Our goal is to detect and localize a wide range of activities, including ...
热度：46

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Challenges for Machine Learning in Computational Sustainability[计算机可持续发展中机器学习面临的挑战]
   Thomas Dietterich(俄勒冈州立大学) Research in computational sustainability seeks to develop and apply methods from computer science to the many challenges of managing the earth's e...
热度：27

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Iterative Learning of Weighted Rule Sets for Greedy Search[贪婪搜索加权规则集的迭代学习]
  Yuehua Xu(俄勒冈州立大学) Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider le...
热度：50

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Steps Toward Robust Artificial Intelligence[迈向强大的人工智能的步骤]
  Thomas G. Dietterich(俄勒冈州立大学) Steps Toward Robust Artificial Intelligence
热度：30

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Machine Learning in Ecosystem Informatics and Sustainability[在生态系统中的信息的机器学习和可持续性]
  Thomas Dietterich, Carlos Guestrin(俄勒冈州立大学) Ecosystem Informatics brings together mathematical and computational tools to address scientific and policy challenges in the ecosystem sciences. Thes...
热度：54

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Adapting the Right Measures for K-Means Clustering[K均值聚类调整合适的措施]
  Junjie Wu(俄勒冈州立大学) Clustering validation is a long standing challenge in the clustering literature. While many validation measures have been developed for evaluating the...
热度：53

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Cost-Sensitive Top-Down/Bottom-Up Inference for Multiscale Activity Recognition[成本敏感的自上而下的/底部的多尺度行为识别推理]
  Ivan Laptev, Michael J. Black, Mohamed Amer(俄勒冈州立大学) This paper addresses a new problem, that of multiscale activity recognition. Our goal is to detect and localize a wide range of activities, including ...
热度：80

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ILP Invited Panel - Structured Machine Learning: The Next 10 Years[ILP邀请小组结构化机器学习：未来10年]
  Thomas Dietterich, Stephen Muggleton, Bernhard Pfahringer, Lise Getoor, Pedro Domingos(俄勒冈州立大学) ILP Invited Panel - Structured Machine Learning: The Next 10 Years.
热度：36

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Declarative Vs. Procedural[陈述Vs程序]
  Thomas Dietterich(俄勒冈州立大学)
热度：33

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Graphical Multi-Task Learning[图形化多任务学习]
  Daniel Sheldon(俄勒冈州立大学)
热度：20
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