Description
Machine Learning & Knowledge Discovery in Databases: Applied Data Science Research (Springer, 2023)
About this book
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.
Overview :
Editors:
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14174)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
Included in the following conference series:
Conference proceedings info: ECML PKDD 2023.
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