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Titlebook: Machine Learning, Optimization, and Data Science; 9th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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發(fā)表于 2025-3-21 17:54:13 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning, Optimization, and Data Science
副標題9th International Co
編輯Giuseppe Nicosia,Varun Ojha,Renato Umeton
視頻videohttp://file.papertrans.cn/621/620739/620739.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning, Optimization, and Data Science; 9th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202
描述.This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023.?.The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributions focus on the state?of the art and the latest advances in the integration of machine learning, deep?learning, nonlinear optimization and data science to provide and support the?scientific and technological foundations for interpretable, explainable and trustworthy AI.?.
出版日期Conference proceedings 2024
關(guān)鍵詞computer security; evolutionary algorithms; fuzzy control; image processing; database systems; artificial
版次1
doihttps://doi.org/10.1007/978-3-031-53969-5
isbn_softcover978-3-031-53968-8
isbn_ebook978-3-031-53969-5Series 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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,Knowledge Distillation with?Segment Anything (SAM) Model for?Planetary Geological Mapping,y training a specialised domain decoder, we can achieve performance comparable to state of the art on this task. Key results indicate that the use of knowledge distillation can significantly reduce the effort required by domain experts for manual annotation and improve the efficiency of image segmen
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,Genetic Programming with?Synthetic Data for?Interpretable Regression Modelling and?Limited Data,rm better than it would if trained on the original data alone. We carry out experiments on four well-known regression datasets comparing results between an initial model and a model trained on the initial model’s outputs; we find some results which are positive for each hypothesis and some which are
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