Muutke küpsiste eelistusi

Advances in Knowledge Discovery and Data Mining: 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 710, 2024, Proceedings, Part II 2024 ed. [Pehme köide]

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 459 pages, kõrgus x laius: 235x155 mm, 138 Illustrations, color; 7 Illustrations, black and white; XXXIV, 459 p. 145 illus., 138 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14646
  • Ilmumisaeg: 26-Apr-2024
  • Kirjastus: Springer Nature
  • ISBN-10: 9819722527
  • ISBN-13: 9789819722525
Teised raamatud teemal:
  • Pehme köide
  • Hind: 76,49 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 89,99 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Advances in Knowledge Discovery and Data Mining: 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 710, 2024, Proceedings, Part II 2024 ed.Suurem pilt
  • Formaat: Paperback / softback, 459 pages, kõrgus x laius: 235x155 mm, 138 Illustrations, color; 7 Illustrations, black and white; XXXIV, 459 p. 145 illus., 138 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14646
  • Ilmumisaeg: 26-Apr-2024
  • Kirjastus: Springer Nature
  • ISBN-10: 9819722527
  • ISBN-13: 9789819722525
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789819722525.html
Märksõnad:
The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 710, 2024.





The 177 papers presented in these proceedings were carefully reviewed and selected from 720 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.