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Titlebook: Engineering Applications of Neural Networks; 23rd International C Lazaros Iliadis,Chrisina Jayne,Elias Pimenidis Conference proceedings 202

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书目名称Engineering Applications of Neural Networks
副标题23rd International C
编辑Lazaros Iliadis,Chrisina Jayne,Elias Pimenidis
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Engineering Applications of Neural Networks; 23rd International C Lazaros Iliadis,Chrisina Jayne,Elias Pimenidis Conference proceedings 202
描述This book constitutes the refereed proceedings of the 23rd International Conference on Engineering Applications of Neural Networks, EANN 2022, held in Chersonisos, Crete, Greece, in June 2022..The 37 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on Bio inspired Modeling / Novel Neural Architectures; Classification / Clustering; Machine Learning; Convolutional / Deep Learning; Datamining / Learning / Autoencoders; Deep Learning / Blockchain; Machine Learning for Medical Images / Genome Classification; Reinforcement /Adversarial / Echo State Neural Networks; Robotics / Autonomous Vehicles, Photonic Neural Networks; Text Classification / Natural Language..
出版日期Conference proceedings 2022
关键词artificial intelligence; computer hardware; computer networks; computer systems; computer vision; correla
版次1
doihttps://doi.org/10.1007/978-3-031-08223-8
isbn_softcover978-3-031-08222-1
isbn_ebook978-3-031-08223-8Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2022
The information of publication is updating

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SNNs Model Analyzing and Visualizing Experimentation Using RAVSimoding method and the nonlinear activated neuron model and transmitting only the binary spike events. However, these complex model simulations and behavioral analysis are a standard approach of parametric values verification prior to their physical implementation on the hardware. Recently some popula
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Complex Layers of Formal Neuronsure complicates the design of classification or regression models..Complex layers of formal neurons (linear classifiers) can be designed on the basis of data sets composed of high-dimensional feature vectors. Linear classifiers of a given complex layer are designed on disjoint subsets of features ob
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Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methodsion forest building techniques, called Maximal Information Coefficient Forest (MICF) and Pearson’s Correlation Coefficient Forest (PCCF). The proposed new algorithms use Pearson’s Correlation Coefficient (PCC) and Maximal Information Coefficient (MIC) as extra measures of the classification capacity
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On the Suitability of Neural Networks as Building Blocks for the Design of Efficient Learned Indexess proper of Data Structures. This new area goes under the name of .. The motivation for its study is a perceived change of paradigm in Computer Architectures that would favour the use of Graphics Processing Units and Tensor Processing Units over conventional Central Processing Units. In turn, that w
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