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

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书目名称Machine Learning and Knowledge Discovery in Databases, Part II
副标题European Conference,
编辑Dimitrios Gunopulos,Thomas Hofmann,Michalis Vazirg
视频video
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Knowledge Discovery in Databases, Part II; European Conference, Dimitrios Gunopulos,Thomas Hofmann,Michalis Vazirg Con
描述This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011.The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.
出版日期Conference proceedings 2011
关键词decision theory; high-dimensional clustering; natural language processing; recommender systems; self-org
版次1
doihttps://doi.org/10.1007/978-3-642-23783-6
isbn_softcover978-3-642-23782-9
isbn_ebook978-3-642-23783-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
The information of publication is updating

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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
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