LEADER 04009 am 22006973u 450 001 9910337835803321 005 20230125200551.0 010 $a3-030-05318-0 024 7 $a10.1007/978-3-030-05318-5 035 $a(CKB)4100000008217382 035 $a(DE-He213)978-3-030-05318-5 035 $a(MiAaPQ)EBC5788944 035 $a(Au-PeEL)EBL5788944 035 $a(OCoLC)1105039769 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/31379 035 $a(PPN)236524313 035 $a(EXLCZ)994100000008217382 100 $a20190517d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomated Machine Learning$b[electronic resource] $eMethods, Systems, Challenges /$fedited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren 205 $a1st ed. 2019. 210 $aCham$cSpringer Nature$d2019 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XIV, 219 p. 54 illus., 45 illus. in color.) 225 1 $aThe Springer Series on Challenges in Machine Learning,$x2520-131X 311 $a3-030-05317-2 327 $a1 Hyperparameter Optimization -- 2 Meta-Learning -- 3 Neural Architecture Search -- 4 Auto-WEKA -- 5 Hyperopt-Sklearn -- 6 Auto-sklearn -- 7 Towards Automatically-Tuned Deep Neural Networks -- 8 TPOT -- 9 The Automatic Statistician -- 10 AutoML Challenges. 330 $aThis open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. 410 0$aThe Springer Series on Challenges in Machine Learning,$x2520-131X 606 $aArtificial intelligence 606 $aOptical data processing 606 $aPattern recognition 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 610 $aComputer science 610 $aArtificial intelligence 610 $aOptical data processing 610 $aPattern recognition 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 0$aPattern recognition. 615 14$aArtificial Intelligence. 615 24$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 676 $a006.3 700 $aHutter$b Frank$4edt$01356285 702 $aHutter$b Frank$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKotthoff$b Lars$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVanschoren$b Joaquin$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337835803321 996 $aAutomated Machine Learning$93360613 997 $aUNINA