LEADER 02072nam 2200433 450 001 9910583305103321 005 20191209204550.0 010 $a0-12-811789-3 010 $a0-12-811788-5 035 $a(CKB)4100000004376101 035 $a(MiAaPQ)EBC5407385 035 $a(PPN)233365923 035 $a(EXLCZ)994100000004376101 100 $a20180619d2018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning techniques for space weather /$fedited by Enrico Camporeale, Simon Wing, Jay R. Johnson 210 1$aAmsterdam, Netherlands :$cElsevier,$d[2018] 210 4$dİ2018 215 $a1 online resource (454 pages) 330 $a"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. 606 $aSpace environment 606 $aMachine learning 615 0$aSpace environment. 615 0$aMachine learning. 676 $a629.416 702 $aWing$b Simon 702 $aJohnson$b Jay 702 $aCamporeale$b Enrico 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910583305103321 996 $aMachine learning techniques for space weather$92224998 997 $aUNINA