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231010 ||| eng |
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|a 9783031434150
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| 100 |
1 |
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|a Koutra, Danai
|e [editor]
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| 245 |
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|a Machine Learning and Knowledge Discovery in Databases: Research Track
|h Elektronische Ressource
|b European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II
|c edited by Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi
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| 250 |
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|a 1st ed. 2023
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| 260 |
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|a Cham
|b Springer Nature Switzerland
|c 2023, 2023
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| 300 |
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|a LIV, 719 p. 309 illus., 177 illus. in color
|b online resource
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| 505 |
0 |
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|a Computer Vision -- Deep Learning -- Fairness -- Federated Learning -- Few-shot learning -- Generative Models -- Graph Contrastive Learning
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| 653 |
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|a Image processing / Digital techniques
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| 653 |
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|a Software engineering
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| 653 |
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|a Computer vision
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| 653 |
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|a Software Engineering
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computers
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| 653 |
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|a Computer networks
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| 653 |
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|a Computer Imaging, Vision, Pattern Recognition and Graphics
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| 653 |
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|a Computer Engineering and Networks
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Computer engineering
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| 653 |
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|a Computing Milieux
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| 700 |
1 |
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|a Plant, Claudia
|e [editor]
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| 700 |
1 |
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|a Gomez Rodriguez, Manuel
|e [editor]
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| 700 |
1 |
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|a Baralis, Elena
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Artificial Intelligence
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-43415-0
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-031-43415-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a 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
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