| 書目名稱 | Proceedings of ELM 2021 | | 副標(biāo)題 | Theory, Algorithms a | | 編輯 | Kaj-Mikael Bj?rk | | 視頻video | http://file.papertrans.cn/758/757558/757558.mp4 | | 概述 | Provides recent research on Extreme Learning Machines (ELM).Contains selected papers from the 11th International Conference on Extreme Learning Machines 2022.Presents theory, algorithms, and applicati | | 叢書名稱 | Proceedings in Adaptation, Learning and Optimization | | 圖書封面 |  | | 描述 | .This book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 15–16, 2021. Extreme learning machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training dataand application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neuron | | 出版日期 | Conference proceedings 2023 | | 關(guān)鍵詞 | Intelligent Systems; Extreme Learning Machine; ELM 2021; The International Conference on Extreme Learni | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-21678-7 | | isbn_softcover | 978-3-031-21680-0 | | isbn_ebook | 978-3-031-21678-7Series ISSN 2363-6084 Series E-ISSN 2363-6092 | | issn_series | 2363-6084 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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