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Machine Learning & Knowledge Discovery in Databases: Applied Data Science Research 2023

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Learn the latest applied data science research in knowledge discovery, databases, and AI-driven insights.

Description

Machine Learning & Knowledge Discovery in Databases: Applied Data Science Research (Springer, 2023)

About this book

Knowledge Discovery AI Guide The 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.

 

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)


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