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[null,"1.","Record Nr.",null,"UNINA9910583305103321"]
[null,null,"Titolo",null,"Machine learning techniques for space weather / / edited by Enrico Camporeale, Simon Wing, Jay R. Johnson"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam, Netherlands : , : Elsevier, , [2018]"]
[{"$nulls":4},"©2018"]
[null,null,"ISBN",null,"0-12-811789-3"]
[{"$nulls":4},"0-12-811788-5"]
[null,null,"Descrizione fisica",null,"1 online resource (454 pages)"]
[null,null,"Disciplina",null,"629.416"]
[null,null,"Soggetti",null,"Space environment"]
[{"$nulls":4},"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","\"A thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields\"--Page 4 of cover."]