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Titlebook: Intelligent Computing Theories and Application; 14th International C De-Shuang Huang,Kang-Hyun Jo,Xiao-Long Zhang Conference proceedings 20

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書目名稱Intelligent Computing Theories and Application
副標(biāo)題14th International C
編輯De-Shuang Huang,Kang-Hyun Jo,Xiao-Long Zhang
視頻videohttp://file.papertrans.cn/470/469480/469480.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Intelligent Computing Theories and Application; 14th International C De-Shuang Huang,Kang-Hyun Jo,Xiao-Long Zhang Conference proceedings 20
描述.This two-volume set LNCS 10954 and LNCS 10955 constitutes - in conjunction with the volume LNAI 10956 - the refereed proceedings of the 14th International Conference on Intelligent Computing, ICIC 2018, held in Wuhan, China, in August 2018. The 275 full papers and 72 short papers of the three proceedings volumes were carefully reviewed and selected from 632 submissions. The papers are organized in topical sections such as Neural Networks.- Pattern Recognition.- Image Processing.- Intelligent Computing in Robotics.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Fuzzy Theory and Algorithms.- Supervised Learning.- Unsupervised Learning.- Kernel Methods and Supporting Vector Machines.- Knowledge Discovery and Data Mining.- Natural Language Processing and Computational Linguistics.- Gene Expression Array Analysis.- Systems Biology.- Computational Genomics.- Computational Proteomics.- Gene Regulation Modeling and Analysis.- Protein-Protein Interaction Prediction.- Next-Gen Sequencing and Metagenomics.- Structure Prediction and Folding.- Evolutionary Optimization for Scheduling.- High-Throughput Biomedical Data Integration and Mining.- Machine Learning A
出版日期Conference proceedings 2018
關(guān)鍵詞Supervised learning; Unsupervised learning; Reinforcement learning; Semi-supervised learning; Data minin
版次1
doihttps://doi.org/10.1007/978-3-319-95933-7
isbn_softcover978-3-319-95932-0
isbn_ebook978-3-319-95933-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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A Fast Algorithm for Image Segmentation Based on Local Chan Vese Model, based on the steepest descent method and finite difference scheme. In this paper, we propose a sweeping algorithm to minimize Local Chan Vese (LCV) model. We calculate the energy change when a pixel is moved from the outside region to the inside region of evolving curves and vice versa, instead of
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