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OverviewThe multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo. Chapter 41 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink. Full Product DetailsAuthor: Danai Koutra , Claudia Plant , Manuel Gomez Rodriguez , Elena BaralisPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 1st ed. 2023 Volume: 14171 Weight: 1.151kg ISBN: 9783031434174ISBN 10: 303143417 Pages: 707 Publication Date: 17 September 2023 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsGraph Neural Networks.- Graphs.- Interpretability.- Knowledge Graphs.- Large-scale Learning.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |