1.

Record Nr.

UNISA996691669503316

Autore

Dutra Inês

Titolo

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track : European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings, Part X / / edited by Inês Dutra, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026

ISBN

3-032-06129-6

Edizione

[1st ed. 2026.]

Descrizione fisica

1 online resource (880 pages)

Collana

Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 16022

Altri autori (Persone)

PechenizkiyMykola

CortezPaulo

PashamiSepideh

PasqualiArian

MonizNuno

JorgeAlípio M

SoaresCarlos

AbreuPedro H

GamaJoão

Disciplina

006.3

Soggetti

Artificial intelligence

Computer networks

Computers

Image processing - Digital techniques

Computer vision

Software engineering

Artificial Intelligence

Computer Communication Networks

Computing Milieux

Computer Imaging, Vision, Pattern Recognition and Graphics

Software Engineering

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



Sommario/riassunto

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.