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Titlebook: Research and Development in Intelligent Systems XXXI; Incorporating Applic Max Bramer,Miltos Petridis Conference proceedings 2014 Springer

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書(shū)目名稱(chēng)Research and Development in Intelligent Systems XXXI
副標(biāo)題Incorporating Applic
編輯Max Bramer,Miltos Petridis
視頻videohttp://file.papertrans.cn/828/827885/827885.mp4
概述This volume contains new, refereed papers presented at AI-2014, the Thirty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence.Presents new and in
圖書(shū)封面Titlebook: Research and Development in Intelligent Systems XXXI; Incorporating Applic Max Bramer,Miltos Petridis Conference proceedings 2014 Springer
描述.The papers in this volume are the refereed papers presented at AI-2014, the Thirty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2014 in both the technical and the application streams..They present new and innovative developments and applications, divided into technical stream sections on Knowledge Discovery and Data Mining, Machine Learning, and Agents, Ontologies and Genetic Programming, followed by application stream sections on Evolutionary Algorithms/Dynamic Modelling, Planning and Optimisation, and Machine Learning and Data Mining. The volume also includes the text of short papers presented as posters at the conference..This is the thirty-first volume in the .Research and Development in Intelligent Systems. series, which also incorporates the twenty-second volume in the .Applications and Innovations in Intelligent Systems. series. These series are essential reading for those who wish to keep up to date with developments in this important field..
出版日期Conference proceedings 2014
關(guān)鍵詞Artificial Intelligence; Intelligent Systems
版次1
doihttps://doi.org/10.1007/978-3-319-12069-0
isbn_softcover978-3-319-12068-3
isbn_ebook978-3-319-12069-0
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Research and Development in Intelligent Systems XXXI978-3-319-12069-0
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Towards a Parallel Computationally Efficient Approach to Scaling Up Data Stream Classificationty to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Nei
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Wael Hamdan,Rady Khazem,Ghaida Rebdawi,Madalina Croitoru,Alain Gutierrez,Patrice Bucheimbalances and the secondary earner question. Female employment recently outperformed male employment, but fiscal consolidation policies currently hinder advancement in countries like the so-called GIPSI group, where progress is needed most. Meanwhile differences with respect to men in pension incom
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