Machine Learning, Optimization, and Data Science : 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I / / edited by Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (667 pages)
• Disciplina: 006.31
• Soggetto topico: Application software; Computer and Information Systems Applications
• Persona (resp. second.): NicosiaGiuseppe
• Nota di contenuto: Deep Learning -- Machine Learning -- Reinforcement Learning -- Neural Networks -- Deep Reinforcement Learning -- Optimization -- Global Optimization -- Multi-Objective Optimization -- Computational Optimization.-Data Science -- Big Data -- Data Analytics -- Artificial Intelligence.
• Sommario/riassunto: This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
• Titolo autorizzato: Machine learning, optimization, and data science
• ISBN: 3-030-95467-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910522560403321
• Lo trovi qui: Univ. Federico II
• Serie: Information Systems and Applications, incl. Internet/Web, and HCI, . 2946-1642 ; ; 13163