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Titlebook: Emerging Intelligent Computing Technology and Applications; 9th International Co De-Shuang Huang,Phalguni Gupta,Michael Gromiha Conference

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发表于 2025-3-21 19:31:20 | 显示全部楼层 |阅读模式
书目名称Emerging Intelligent Computing Technology and Applications
副标题9th International Co
编辑De-Shuang Huang,Phalguni Gupta,Michael Gromiha
视频video
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Emerging Intelligent Computing Technology and Applications; 9th International Co De-Shuang Huang,Phalguni Gupta,Michael Gromiha Conference
描述This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Computing, ICIC 2013, held in Nanning, China, in July 2013. The 192 revised full papers presented in the three volumes LNCS 7995, LNAI 7996, and CCIS 375 were carefully reviewed and selected from 561 submissions. The papers in this volume (CCIS 375) are organized in topical sections on Neural Networks; Systems Biology and Computational Biology; Computational Genomics and Proteomics; Knowledge Discovery and Data Mining; Evolutionary Learning and Genetic Algorithms; Machine Learning Theory and Methods; Biomedical Informatics Theory and Methods; Particle Swarm Optimization and Niche Technology; Unsupervised and Reinforcement Learning; Intelligent Computing in Bioinformatics; Intelligent Computing in Finance/Banking; Intelligent Computing in Petri Nets/Transportation Systems; Intelligent Computing in Signal Processing; Intelligent Computing in Pattern Recognition; Intelligent Computing in Image Processing; Intelligent Computing in Robotics; Intelligent Computing in Computer Vision; Special Session on Biometrics System and Security for Intelligent Computing; Special Session on Bio-inspired
出版日期Conference proceedings 2013
关键词algorithm model; bio inspired computation; image processing and pattern recognition; machine learning; m
版次1
doihttps://doi.org/10.1007/978-3-642-39678-6
isbn_softcover978-3-642-39677-9
isbn_ebook978-3-642-39678-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE
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https://doi.org/10.1007/978-3-322-80407-5nential stability of these networks. The derived result improves and extends some related results in the literature. Finally, an illustrative example is provided to demonstrate the effectiveness of our theoretical results.
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Karl Steinbuch,Werner Rupprechtclassification error of the model. We show the performance of the multi-objectivization approach on five data sets and compare it to a surrogate based single-objective algorithm for the same problem. Moreover, we compare the multi-objectivization approach to two surrogate based approaches – a single-objective one and a multi-objective one.
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Karl Steinbuch,Werner Rupprechtng and testing of two well known classifiers- Extreme Learning Machine (ELM) and Support Vector Machine (SVM). The performance evaluation of ELM and SVM classifiers shows that the proposed feature selection method works well with these classifiers.
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,Digitale Übertragung im Basisband,an three previously published differential coexpression analysis (DCEA) methods. We applied the new approach to a public available type 2 diabetes (T2D) expression dataset, and many additional discoveries can be found through our method.
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