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Titlebook: Machine Learning, Optimization, and Data Science; 8th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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書目名稱Machine Learning, Optimization, and Data Science
副標(biāo)題8th International Co
編輯Giuseppe Nicosia,Varun Ojha,Renato Umeton
視頻videohttp://file.papertrans.cn/621/620743/620743.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning, Optimization, and Data Science; 8th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202
描述This two-volume set, LNCS 13810 and 13811,? constitutes the refereed proceedings of the 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, together with the papers of the Second Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022.. The total of 84 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 226 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
出版日期Conference proceedings 2023
關(guān)鍵詞adaptive control systems; anomaly detection; artificial intelligence; automation; bayesian networks; big
版次1
doihttps://doi.org/10.1007/978-3-031-25599-1
isbn_softcover978-3-031-25598-4
isbn_ebook978-3-031-25599-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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,Machine Learning Approaches for?Predicting Crystal Systems: A Brief Review and?a?Case Study,rediction, grouped according to the input features they use to construct the prediction model. It also presents the results obtained in predicting the crystal system of polycrystalline compounds, by using the lattice parameters to train some learning models.
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,Local Optimisation of?Nystr?m Samples Through Stochastic Gradient Descent, we discuss how Nystr?m samples can be efficiently optimised through stochastic gradient descent. We perform numerical experiments which demonstrate that the local minimisation of the radial SKD yields Nystr?m samples with improved Nystr?m approximation accuracy in terms of trace, Frobenius and spectral norms.
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Conference proceedings 2023rticles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
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