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Könyv Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track Pedro H. Abreu

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track

Szerző: Pedro H. Abreu, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Alípio M. Jorge, Carlos Soares, Joao Gama
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Springer-Verlag GmbH
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
30 795 Ft
This multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the Europea...

Információk a könyvről

Szerző
Pedro H. Abreu, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Alípio M. Jorge, Carlos Soares, Joao Gama
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2025
oldal
604
EAN
9783032061287
ISBN
3032061288
Enbook ID
49478149
Kiadó
Springer-Verlag GmbH
Súly
834
Méretek
155 x 235

Teljes leírás

This multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2025, held in Porto, Portugal, September 15 19, 2025.

The 300 full papers presented here, together with 15 demo papers, were carefully reviewed and selected from 1253 submissions. The papers presented in these proceedings are from the following three conference tracks:

  • The Research Track in Volume LNAI 16013-16020 refers about Anomaly & Outlier Detection, Bias & Fairness, Causality, Clustering, Data Challenges, Diffusion Models, Ensemble Learning, Graph Neural Networks, Graphs & Networks, Healthcare & Bioinformatics, Images & Computer Vision, Interpretability & Explainability, Large Language Models, Learning Theory, Multimodal Data, Neuro Symbolic Approaches, Optimization, Privacy & Security, Recommender Systems, Reinforcement Learning, Representation Learning, Resource Efficiency, Robustness & Uncertainty, Sequence Models, Streaming & Spatiotemporal Data, Text & Natural Language Processing, Time Series, and Transfer & Multitask Learning.
  • The Applied Data Science Track in Volume LNAI 16020-16022 refers about Agriculture, Food and Earth Sciences, Education, Engineering and Technology, Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Industry (4.0, 5.0, Manufacturing, ...), Smart Cities, Transportation and Utilities (e.g., Energy), Sports, and Web and Social Networks
  • The Demo Track in LNAI 16022 showcased practical applications and prototypes, accepting 15 papers from a total of 30 submissions. These proceedings cover the papers accepted in the research and applied data science tracks.

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