图 书 馆开课单位--鲁汶大学
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11Probabilistic Topic Modeling in Multilingual Settings: A Short Overview of Its Methodology and Applications[多语种背景下的概率主题建模：方法与应用概述]
Ivan Vulić(鲁汶大学) Probabilistic topic models are unsupervised generative models that model document content as a two-step generation process, i.e., documents are observ...
热度：53
Ivan Vulić(鲁汶大学) Probabilistic topic models are unsupervised generative models that model document content as a two-step generation process, i.e., documents are observ...
热度：53
12Has lexicography reaped the full benefit of the (learner) corpus revolution?[词典编纂学是否已获得（学习者）语料库革命的全部好处？]
Sylviane Granger(鲁汶大学) In 1992 Rundell and Stock wrote an extended three-part article on the “corpus revolution”, in which they describe the rise of corpora and ...
热度：71
Sylviane Granger(鲁汶大学) In 1992 Rundell and Stock wrote an extended three-part article on the “corpus revolution”, in which they describe the rise of corpora and ...
热度：71
13Architecture, Power, Embodiment[建筑，力量，体现]
Hilde Heynen(鲁汶大学) This lecture traces how architecture is intertwined with articulations of power and difference by reviewing the relevant literature of recent decades....
热度：78
Hilde Heynen(鲁汶大学) This lecture traces how architecture is intertwined with articulations of power and difference by reviewing the relevant literature of recent decades....
热度：78
14Mining Sets of Patterns[模式挖掘]
Siegfried Nijssen; Jilles Vreeken; Bjorn Bringmann(鲁汶大学) Pattern mining is one of the most important topics in data mining. The core idea is to extract relevant 'nuggets' of knowledge describing part...
热度：23
Siegfried Nijssen; Jilles Vreeken; Bjorn Bringmann(鲁汶大学) Pattern mining is one of the most important topics in data mining. The core idea is to extract relevant 'nuggets' of knowledge describing part...
热度：23
15State-merging DFA induction algorithms with mandatory merge constraints[状态合并的DFA感应算法约束强制合并]
Pierre Dupont(鲁汶大学) It has been claimed that the hardest par t in building a softwaresystem is deciding precisely what the system should do；One can automate parts of thi...
热度：50
Pierre Dupont(鲁汶大学) It has been claimed that the hardest par t in building a softwaresystem is deciding precisely what the system should do；One can automate parts of thi...
热度：50
16Generating Diverse Realistic Data Sets for Episode Mining[用于集挖掘的各种现实数据集生成]
Albrecht Zimmermann(鲁汶大学)
热度：30
Albrecht Zimmermann(鲁汶大学)
热度：30
17Identification of overlapping biclusters using probabilistic relational models[利用概率关系模型重叠biclusters鉴定]
Hui Zhao(鲁汶大学) coauthor: Hui Zhao, KU Leuven published: Nov. 20, 2007, recorded: September 2007, views: 184
热度：38
Hui Zhao(鲁汶大学) coauthor: Hui Zhao, KU Leuven published: Nov. 20, 2007, recorded: September 2007, views: 184
热度：38
18Speeding up Feature Subset Selection through Mutual Information Relevance Filtering[通过互信息关联滤波加快特征子集选择]
Gert Van Dijck(鲁汶大学)
热度：25
Gert Van Dijck(鲁汶大学)
热度：25
19Face detection without bells and whistles[人脸检测，无花哨]
Markus Mathias(鲁汶大学) Face detection is a mature problem in computer vision. While diverse high performing face detectors have been proposed in the past, we present two sur...
热度：51
Markus Mathias(鲁汶大学) Face detection is a mature problem in computer vision. While diverse high performing face detectors have been proposed in the past, we present two sur...
热度：51
20Learning the Parameters of Probabilistic Logic Programs from Interpretations[从解释学概率逻辑程序的参数]
Ingo Thon(鲁汶大学) ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probabilit...
热度：64
Ingo Thon(鲁汶大学) ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probabilit...
热度：64