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Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2023; 24th International C Paulo Quaresma,David Camacho,Antonio J. Tallón-Bal C

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书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2023
副标题24th International C
编辑Paulo Quaresma,David Camacho,Antonio J. Tallón-Bal
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2023; 24th International C Paulo Quaresma,David Camacho,Antonio J. Tallón-Bal C
描述This book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Évora, Portugal, during November 22–24, 2023..The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI..The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning..
出版日期Conference proceedings 2023
关键词applied computing; bioinformatics; computer vision; computing methodologies; data analytics; data managem
版次1
doihttps://doi.org/10.1007/978-3-031-48232-8
isbn_softcover978-3-031-48231-1
isbn_ebook978-3-031-48232-8Series 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
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Evaluating Text Classification in the Legal Domain Using BERT Embeddingss, SVM, neural networks, CNNs, and deep learning architectures. Despite BERT’s state-of-the-art capabilities, TF-IDF outperformed neural techniques across considered metrics. High similarity between certain classes was hypothesized to hinder BERT’s contextual embedding.
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