Advances in Knowledge Discovery and Data Mining 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10–13, 2025, Proceedings, Part IV /

The five-volume set, LNAI 158710 - 15874 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10–13, 2025. The conference received a total of 557 submissions to the main track, 35 s...

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Yhteisötekijä: SpringerLink (Online service)
Muut tekijät: Wu, Xintao (Toimittaja), Spiliopoulou, Myra (Toimittaja), Wang, Can (Toimittaja), Kumar, Vipin (Toimittaja), Cao, Longbing (Toimittaja), Wu, Yanqiu (Toimittaja), Yao, Yu (Toimittaja), Wu, Zhangkai (Toimittaja)
Aineistotyyppi: Elektroninen E-kirja
Kieli:englanti
Julkaistu: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Painos:1st ed. 2025.
Sarja:Lecture Notes in Artificial Intelligence, 15873
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Linkit:https://doi.org/10.1007/978-981-96-8183-9
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520 |a The five-volume set, LNAI 158710 - 15874 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10–13, 2025. The conference received a total of 557 submissions to the main track, 35 submissions to the survey track and 104 submittion to the special track on LLMs. Of these, 134 papers have been accepted for the main track, 10 for the survey track and 24 for the LLM track. 68 papers have been transferred to the4 DSFA special session. The papers have been organized in topical sections as follows: Part I: Anomaly Detection; Business Data Analysis; Clustering; Continual Learning; Contrastive Learning; Data Processing for Learning; Part II: Fairness and Interpretability; Federated Learning; Graph Mining and GNN; Learning on Scientific Data; Part III: Machine Learning; Multi-modality; OOD and Optimization; Recommender Systems; Representation Learning and Generative AI; Part IV: Security and Privacy; Temporal Learning; Survey; Part V: LLM Fine-tuning and Prompt Engineering; Fairness and Interpretability of LLMs; LLM Application; OOD and Optimization of LLMs. 
532 8 |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. 
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650 0 |a Artificial intelligence. 
650 0 |a Algorithms. 
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650 2 4 |a Mathematics of Computing. 
650 2 4 |a Signal, Speech and Image Processing. 
650 2 4 |a Computer Communication Networks. 
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700 1 |a Wang, Can.  |e editor.  |0 (orcid)0000-0002-2890-0057  |1 https://orcid.org/0000-0002-2890-0057  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Kumar, Vipin.  |e editor.  |0 (orcid)0000-0002-9040-2665  |1 https://orcid.org/0000-0002-9040-2665  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Cao, Longbing.  |e editor.  |0 (orcid)0000-0003-1524-9063  |1 https://orcid.org/0000-0003-1524-9063  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
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700 1 |a Yao, Yu.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
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