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Titlebook: Computational Collective Intelligence; 9th International Co Ngoc Thanh Nguyen,George A. Papadopoulos,Gottfried Conference proceedings 2017

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發(fā)表于 2025-3-21 19:41:35 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computational Collective Intelligence
副標題9th International Co
編輯Ngoc Thanh Nguyen,George A. Papadopoulos,Gottfried
視頻videohttp://file.papertrans.cn/233/232179/232179.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Collective Intelligence; 9th International Co Ngoc Thanh Nguyen,George A. Papadopoulos,Gottfried Conference proceedings 2017
描述This two-volume ?set (LNAI 10448 and LNAI 10449) constitutes the refereed proceedings of the 9th International Conference on Collective Intelligence, ICCCI 2017, held in Nicosia, Cyprus, in September 2017..The 117 full papers presented were carefully reviewed and selected from 248 submissions. The conference focuseson?the methodology and applications of computational?collective intelligence, included: multi-agent systems, knowledge engineering and?semantic web, social networks and recommender systems, text processing and?information retrieval, data mining methods and applications, sensor networks?and internet of things, decision support & control systems, and computer vision?techniques.?
出版日期Conference proceedings 2017
關鍵詞artificial intelligence; internet; learning systems; sensors; world wide web; data mining; image processin
版次1
doihttps://doi.org/10.1007/978-3-319-67077-5
isbn_softcover978-3-319-67076-8
isbn_ebook978-3-319-67077-5Series 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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978-3-319-67076-8Springer International Publishing AG 2017
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Parity Expanded Variational Analysis, classifiers involves evolving the meta-gene using genes induced by GEP from randomly generated combinations of instances with randomly selected subsets of attributes. The main property of the discussed classifier is its scalability allowing adaptation to the size of the dataset under consideration.
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https://doi.org/10.1007/978-1-4614-6387-0tion. For this purpose, first, main concepts of drives, rewards, action selection under modular reinforcement learning as well as an adaptive priority process are developed. Second, experiments and results are presented and analyzed demonstrating the efficiency of the suggested concepts. Finally, a
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Structure of Turbulence and Drag Reductiony. The study was based on a developed cellular automaton (CA) model and the implemented simulation system. The developed CA model extends the basic traffic model with a braking mechanism. It also reflects the actual technical conditions of vehicles (acceleration and braking depending on the dimensio
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https://doi.org/10.1007/978-3-642-52758-6s of different studies for tens of years. The dynamics of opinions change is usually adopted from ideas related to the physical description of magnetism including especially some form of interaction between spins. In our paper we are going to propose the scheme based on formulation of popular global
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https://doi.org/10.1007/978-3-642-52758-6fect the performance of Memetic Algorithm (MA) solving Asymmetric Traveling Salesman Problem (ATSP). This study contains a comparison of quality of solutions obtained (both in terms of the value of the objective function and the performance time of the method) by sixteen variants of the Memetic Algo
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