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派博传思国际中心 »期刊书目

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Titlebook: Intelligent Computing Theory; 10th International C De-Shuang Huang,Vitoantonio Bevilacqua,Prashan Pre Conference proceedings 2014 Springer

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发表于 2025-3-21 18:49:21 | 显示全部楼层 |阅读模式
书目名称Intelligent Computing Theory
副标题10th International C
编辑De-Shuang Huang,Vitoantonio Bevilacqua,Prashan Pre
视频videohttp://file.papertrans.cn/470/469495/469495.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligent Computing Theory; 10th International C De-Shuang Huang,Vitoantonio Bevilacqua,Prashan Pre Conference proceedings 2014 Springer
描述This book – in conjunction with the volumes LNAI 8589 and LNBI 8590 – constitutes the refereed proceedings of the 10th International Conference on Intelligent Computing, ICIC 2014, held in Taiyuan, China, in August 2014. The 92 papers of this volume were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections such as evolutionary computation and learning; swarm intelligence and optimization; machine learning; social and natural computing; neural networks; biometrics recognition; image processing; information security; virtual reality and human-computer interaction; knowledge discovery and data mining; signal processing; pattern recognition; biometric system and security for intelligent computing.
出版日期Conference proceedings 2014
关键词bayesian networks; biometrics; cloud computing; collaborative filtering; computer vision; data mining; den
版次1
doihttps://doi.org/10.1007/978-3-319-09333-8
isbn_softcover978-3-319-09332-1
isbn_ebook978-3-319-09333-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
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GA-EAM Based Hybrid Algorithmy exploring the whole search space. For example Genetic Algorithm (GA) is good in exploitation whereas the Environmental Adaption Method (EAM) performs well in exploring the whole search space. Individually these algorithms have some limitations. In this paper a new hybrid algorithm has been propose
BANAL
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合适
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Parameters Selection for Support Vector Machine Based on Particle Swarm OptimizationM classifier. Authors have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function. The experiments are conducted on the basis of benchmark dataset. Fourteen obtained results clearly confirm the superiority of the PSO-SVM approach.
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Adaptive Hybrid Artificial Fish School Algorithm for Solving the Real Roots of Polynomialsembedded in artificial fish swarm algorithm (AFSA) to speed up the local search. Then the paper Uses AHAFSA to solve the real roots of polynomials, the numerical experiment results show that the AHAFSA not only can effectively locate the global optimum, but also have a rather high convergence speed.
Insatiable
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Optimization of Moving Objects Trajectory Using Particle Filtern provided by the measurements and improve overall estimation observability. We assume that small UFOs equipped with camera and navigation sensors are using for improvement of target tracking and an accurate target location estimate. The UFO trajectory optimization is performed for stationary target
Chronological
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