Muutke küpsiste eelistusi

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 1923, 2022, Proceedings, Part V 1st ed. 2023 [Pehme köide]

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 633 pages, kõrgus x laius: 235x155 mm, kaal: 1027 g, 140 Illustrations, color; 23 Illustrations, black and white; XLVI, 633 p. 163 illus., 140 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 13717
  • Ilmumisaeg: 17-Mar-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031264185
  • ISBN-13: 9783031264184
  • Pehme köide
  • Hind: 85,76 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 100,89 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 633 pages, kõrgus x laius: 235x155 mm, kaal: 1027 g, 140 Illustrations, color; 23 Illustrations, black and white; XLVI, 633 p. 163 illus., 140 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 13717
  • Ilmumisaeg: 17-Mar-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031264185
  • ISBN-13: 9783031264184
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.





The volumes are organized in topical sections as follows:





Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning;





Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems;





Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search;





Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .





Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability;





Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
Supervised learning.- Probabilistic inference.- Optimal transport.- Optimization.- Quantum, hardware.- Sustainability.