Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2026, Naples, Italy, September 7 11, 2026, . (Lecture Notes in Computer Science, 16941)

Ceci, Michelangelo (Editor)/ Masciari, Elio (Editor)/ Manco, Giuseppe (Editor)/ Kocev, Dragi (Editor)/ Vens, Celine (Editor)/ Monreale, Anna (Editor)/ Flach, Peter (Editor)/ Spiliopoulou, Myra (Editor)

ISBN 10: 303237653X / ISBN 13: 9783032376534

Edité par Springer, 2026

A propos de cet article

780 pages. 6.14x1.56x9.21 inches. In Stock.

N° de réf. du vendeur x-303237653X

Détails bibliographiques

• Titre: Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2026, Naples, Italy, September 7 11, 2026, . (Lecture Notes in Computer Science, 16941)
• Éditeur: Springer
• Date de parution: 2026
• Langue: anglais
• ISBN à 10 chiffres: 303237653X
• ISBN à 13 chiffres: 9783032376534
• Reliure: Paperback
• État de l'article: Brand New
• Poids: 1,08 kilogramme

A propos de ce titre

Synopsis

This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.

The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:

• The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;
• The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;
• The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.