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Machine Learning and Knowledge Discovery in Databases: Research Track [electronic resource] : European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part V / edited by Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi.

Contributor(s): Koutra, Danai [editor.] | Plant, Claudia [editor.] | Gomez Rodriguez, Manuel [editor.] | Baralis, Elena [editor.] | Bonchi, Francesco [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 14173Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: LIII, 460 p. 113 illus., 110 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031434242.Subject(s): Artificial intelligence | Computer engineering | Computer networks  | Computers | Image processing -- Digital techniques | Computer vision | Software engineering | Artificial Intelligence | Computer Engineering and Networks | Computing Milieux | Computer Imaging, Vision, Pattern Recognition and Graphics | Software EngineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Robustness -- Time Series -- Transfer and Multitask Learning.
In: Springer Nature eBookSummary: 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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Robustness -- Time Series -- Transfer and Multitask Learning.

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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