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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track [electronic resource] : European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part VI / edited by Gianmarco De Francisci Morales, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis, Elena Baralis, Francesco Bonchi.

TextoSeries Lecture Notes in Artificial Intelligence ; 14174Editor: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: LV, 703 p. 232 illus., 177 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031434273Tema(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 EngineeringFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q334-342TA347.A78Recursos en línea: Libro electrónico Texto

Contenidos:

Applied Machine Learning -- Computational Social Sciences -- Finance -- Hardware and Systems -- Healthcare & Bioinformatics -- Human-Computer Interaction -- Recommendation and Information Retrieval.

En: Springer Nature eBookResumen: 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.

Existencias ( 1 )
Notas de título ( 4 )

Existencias
Tipo de ítem	Biblioteca actual	Colección	Signatura	Copia número	Estado	Fecha de vencimiento	Código de barras
Libro Electrónico	Biblioteca Electrónica	Colección de Libros Electrónicos		1	No para préstamo

Acceso multiusuario

Applied Machine Learning -- Computational Social Sciences -- Finance -- Hardware and Systems -- Healthcare & Bioinformatics -- Human-Computer Interaction -- Recommendation and Information Retrieval.

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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