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Advances in Knowledge Discovery and Data Mining: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 1013, 2025, Proceedings, Part III [Mīkstie vāki]

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  • Formāts: Paperback / softback, 495 pages, height x width: 235x155 mm, 120 Illustrations, color; 10 Illustrations, black and white; XXX, 495 p. 130 illus., 120 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 15872
  • Izdošanas datums: 20-Jun-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819681790
  • ISBN-13: 9789819681792
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Advances in Knowledge Discovery and Data Mining: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 1013, 2025, Proceedings, Part IIIPalielināt
  • Formāts: Paperback / softback, 495 pages, height x width: 235x155 mm, 120 Illustrations, color; 10 Illustrations, black and white; XXX, 495 p. 130 illus., 120 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 15872
  • Izdošanas datums: 20-Jun-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819681790
  • ISBN-13: 9789819681792
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789819681792.html
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 1013, 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.