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1. |
Record Nr. |
UNISALENTO991003299389707536 |
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Autore |
Salsa, Sandro |
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Titolo |
Modelli dinamici e controllo ottimo : un'introduzione elementare / Sandro Salsa, Annamaria Squellati |
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Pubbl/distr/stampa |
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ISBN |
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Descrizione fisica |
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Collana |
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Altri autori (Persone) |
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Squellati, Annamariaauthor |
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Disciplina |
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Soggetti |
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Equazioni differenziali - Manuali |
Calcolo delle variazioni - Manuali |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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2. |
Record Nr. |
UNINA9910647787903321 |
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Titolo |
Fusion of Machine Learning Paradigms : Theory and Applications / / edited by Ioannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (204 pages) |
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Collana |
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Intelligent Systems Reference Library, , 1868-4408 ; ; 236 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Machine learning |
Data protection |
Computational Intelligence |
Artificial Intelligence |
Machine Learning |
Data and Information Security |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Editorial Note -- Artificial Intelligence as Dual-Use Technology -- Diabetic Retinopathy Detection using Transfer and Reinforcement Learning with effective image preprocessing and data augmentation techniques. . |
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Sommario/riassunto |
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This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The |
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book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them. |
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