Titlebook: Machine Learning and Knowledge Discovery in Databases, Part II; European Conference, Dimitrios Gunopulos,Thomas Hofmann,Michalis Vazirg Con

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書目名稱Machine Learning and Knowledge Discovery in Databases, Part II影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II影響因子(影響力)學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II網(wǎng)絡公開度




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II網(wǎng)絡公開度學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II被引頻次學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II年度引用




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II年度引用學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II讀者反饋




書目名稱Machine Learning and Knowledge Discovery in Databases, Part II讀者反饋學科排名

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Mining Research Topic-Related Influence between Academia and Industrys how influence, ideas, information propagate in the network. Similar problems have been proposed on co-authorship networks where the goal is to differentiate the social influences on research topic level and quantify the strength of the influence. In this work, we are interested in the problem of m

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Typology of Mixed-Membership Models: Towards a Design Methodctures with structures known or assumed in the data, we propose how models can be constructed in a controlled way, using the numerical properties of data likelihood and Gibbs full conditionals as predictors of model behavior. To illustrate this “bottom-up” design method, example models are construct

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Online Structure Learning for Markov Logic Networksfor large datasets with thousands of training examples which may not even all fit in main memory. To address this issue, previous work has used online learning to train MLNs. However, they all assume that the model’s structure (set of logical clauses) is given, and only learn the model’s parameters.

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Motion Segmentation by Model-Based Clustering of Incomplete Trajectories contribution of our method is that the trajectories are automatically extracted from the video sequence and they are provided directly to a model-based clustering approach. In most other methodologies, the latter constitutes a difficult part since the resulting feature trajectories have a short dur