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Titlebook: Discovery Science; 19th International C Toon Calders,Michelangelo Ceci,Donato Malerba Conference proceedings 2016 Springer International Pu

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书目名称Discovery Science
副标题19th International C
编辑Toon Calders,Michelangelo Ceci,Donato Malerba
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Discovery Science; 19th International C Toon Calders,Michelangelo Ceci,Donato Malerba Conference proceedings 2016 Springer International Pu
描述This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2016, held in banff, AB, Canada in October 2015. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefullyreviewed and selected from 60 submissions.The conference focuses on following topics: Advances in the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scientific domains..
出版日期Conference proceedings 2016
关键词data mining; evolving networks; knowledge discovery; online social networks; pattern mining; algorithm an
版次1
doihttps://doi.org/10.1007/978-3-319-46307-0
isbn_softcover978-3-319-46306-3
isbn_ebook978-3-319-46307-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototypexisting software, whereas more complex classifiers would require costly software adaptations. In order to predict a time series of instances, we construct a meta classification layer. We then evaluate our model on the data of 180 locomotive tours by leave one out classification. The results show tha
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https://doi.org/10.1007/978-3-658-01919-8of focusing on attribute subset selection, we explore an alternative promising approach consisting of using all available textual information. The problem of bug-fix time estimation is then mapped to a text categorization problem. We consider a multi-topic Supervised Latent Dirichlet Allocation (.)
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Second-Order-Faktorenanalyse (SFA)r law and the number of edges increase as a function of time. Therefore, we discuss a sequential sampling method with forgetting factor to sample the evolving ego network stream. This method captures the most active and recent nodes from the network while preserving the tie strengths between them an
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