1 option
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.
Springer Nature - Springer Computer Science eBooks 2026 English International Available online
View online- Format:
- Book
- Author/Creator:
- Dutra, Inês.
- Series:
- Lecture Notes in Artificial Intelligence, 2945-9141 ; 16022
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Computer networks.
- Computers.
- Image processing--Digital techniques.
- Image processing.
- Computer vision.
- Software engineering.
- Artificial Intelligence.
- Computer Communication Networks.
- Computing Milieux.
- Computer Imaging, Vision, Pattern Recognition and Graphics.
- Software Engineering.
- Local Subjects:
- Artificial Intelligence.
- Computer Communication Networks.
- Computing Milieux.
- Computer Imaging, Vision, Pattern Recognition and Graphics.
- Software Engineering.
- Physical Description:
- 1 online resource (880 pages)
- Edition:
- 1st ed. 2026.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2026.
- Summary:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-032-06129-6
- 9783032061294
- OCLC:
- 1547927518
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.