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Titlebook: Inductive Logic Programming; 26th International C James Cussens,Alessandra Russo Conference proceedings 2017 Springer International Publish

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發(fā)表于 2025-3-21 16:32:52 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Inductive Logic Programming
副標(biāo)題26th International C
編輯James Cussens,Alessandra Russo
視頻videohttp://file.papertrans.cn/464/463886/463886.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Inductive Logic Programming; 26th International C James Cussens,Alessandra Russo Conference proceedings 2017 Springer International Publish
描述This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Conference on Inductive Logic Programming, ILP 2016, held in London, UK, in September 2016.. The 10 full papers presented were carefully reviewed and selected from 29 submissions. The papers represent well the current breath of ILP research topics such as predicate invention; graph-based learning; spatial learning; logical foundations; statistical relational learning; probabilistic ILP; implementation and scalability; applications in robotics, cyber security and games..
出版日期Conference proceedings 2017
關(guān)鍵詞Inductive logic programming; Machine learning; Relational learning; Statistical relational learning; Gra
版次1
doihttps://doi.org/10.1007/978-3-319-63342-8
isbn_softcover978-3-319-63341-1
isbn_ebook978-3-319-63342-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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https://doi.org/10.1007/978-3-319-63342-8Inductive logic programming; Machine learning; Relational learning; Statistical relational learning; Gra
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James Cussens,Alessandra RussoIncludes supplementary material:
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/i/image/463886.jpg
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Learning Predictive Categories Using Lifted Relational Neural Networks,Lifted relational neural networks (LRNNs) are a flexible neural-symbolic framework based on the idea of lifted modelling. In this paper we show how LRNNs can be easily used to specify declaratively and solve learning problems in which latent categories of entities, properties and relations need to be jointly induced.
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