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Titlebook: Intelligent Computing Methodologies; 10th International C De-Shuang Huang,Kang-Hyun Jo,Ling Wang Conference proceedings 2014 Springer Inter

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发表于 2025-3-21 19:38:14 | 显示全部楼层 |阅读模式
书目名称Intelligent Computing Methodologies
副标题10th International C
编辑De-Shuang Huang,Kang-Hyun Jo,Ling Wang
视频videohttp://file.papertrans.cn/470/469459/469459.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligent Computing Methodologies; 10th International C De-Shuang Huang,Kang-Hyun Jo,Ling Wang Conference proceedings 2014 Springer Inter
描述This book – in conjunction with the volumes LNCS 8588 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 85 papers of this volume were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections such as soft computing; artificial bee colony algorithms; unsupervised learning; kernel methods and supporting vector machines; machine learning; fuzzy theory and algorithms; image processing; intelligent computing in computer vision; intelligent computing in communication networks; intelligent image/document retrievals; intelligent data analysis and prediction; intelligent agent and Web applications; intelligent fault diagnosis; knowledge representation/reasoning; knowledge discovery and data mining; natural language processing and computational linguistics; next gen sequencing and metagenomics; intelligent computing in scheduling and engineering optimization; advanced modeling, control and optimization techniques for complex engineering systems; complex networks and their applications; time series forecasting and analysis using
出版日期Conference proceedings 2014
关键词Web crawling; authentication; bayesian network; biometrics; cloud computing; cluster and classification; c
版次1
doihttps://doi.org/10.1007/978-3-319-09339-0
isbn_softcover978-3-319-09338-3
isbn_ebook978-3-319-09339-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Solving Three-Objective Flow Shop Problem with Fast Hypervolume-Based Local Search Algorithmng computation of hypervolume contribution. In the algorithm, we define an approximate hypervolume contribution indicator as the selection mechanism and apply this indicator to an iterated local search. We carry out a range of experiments on three-objective flow shop problem. Experimental results in
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A Learning Automata-Based Singular Value Decomposition and Its Application in Recommendation Systemd at the same time bringing large profits to e-commerce companies. Till now many different recommendation algorithm have been proposed and achieved good effect. In the context Netflix Prize in 2006, Simon Funk proposed a matrix factorization-based recommendation algorithm named Funk-SVD, which cause
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A Novel 2-Stage Combining Classifier Model with Stacking and Genetic Algorithm Based Feature Selectia is first generated by stacking on the original data (called Level0 data) with base classifiers. Level1data is then classified by a second classifier (denoted by C) with feature selection using GA. The advantage of applying GA on Level1 data is that it has lower dimension and is more uniformity tha
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Improved Bayesian Network Structure Learning with Node Ordering via K2 Algorithmm can reduce search space effectively, improve learning efficiency, but it requires the initial node ordering as input, which is very limited by the absence of the priori information. On the other hand, search process of K2 algorithm uses a greedy search strategy and solutions are easy to fall into
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Comparison of EM-Based Algorithms and Image Segmentation Evaluatione model depends on unobserved latent variables. The idea behind the EM algorithm is intuitive and natural, which makes it applicable to a variety of problems. However, the EM algorithm does not guarantee convergence to the global maximum when there are multiple local maxima. In this paper, a random
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The Equivalence Relationship between Kernel Functions Based on SVM and Four-Layer Functional Networkl network and kernel functions based SVM, and the equivalent relationship between functional networks with SVM is demonstrated. This result provides us a very useful guideline when we perform theoretical research and applications on design SVM, functional network systems.
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