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:Â Active Learning;Â Adversarial Machine Learning;Â Anomaly Detection; Applications; Bayesian Methods; Causality;Â Â 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 :Â
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.
Categories: Artificial Intelligence & Machine Learning, Data Science & Analytics, E-Books & PDF GuidesTags: AI research, Big Data, Conference, Data Science, ECML PKDD, knowledge discovery, Machine Learning
Discover more from Expert Training
Subscribe to get the latest posts sent to your email.
Reviews
There are no reviews yet.