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

Name: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
Brand: Springer-Verlag GmbH
SKU: 9783032061287
Price: 342.77 PLN
Availability: InStock

Autor: Pedro H. Abreu, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Alípio M. Jorge, Carlos Soares, Joao Gama

Język:

Oprawa: Miękka

Wydawca: Springer-Verlag GmbH

Dostępność: Dostępna u dostawcy

Wysyłamy za 5-8 dni

342.77 zł

This multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the Europea...

Informacje o książce

Autor

Pedro H. Abreu, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Alípio M. Jorge, Carlos Soares, Joao Gama

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2025

strony

604

EAN

9783032061287

ISBN

3032061288

Enbook ID

49478149

Wydawca

Springer-Verlag GmbH

Waga

834

Wymiary

155 x 235

Pełny opis

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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