LEADER 00934nam0-22003131i-450 001 990007388700403321 005 20230112154658.0 010 $a1-886969-23-X 035 $a000738870 035 $aFED01000738870 035 $a(Aleph)000738870FED01 035 $a000738870 100 $a20030203d2002----km-y0itay50------ba 101 1 $aeng 102 $aUS 105 $aac--ae--001yy 200 1 $aTai Chi walking$ea low-impact path to better health$fRobert Chuckrow 210 $aBoston, Mass.$cYMAA Publication Center$dŠ2002 215 $aXII, 138 p.$cill. in b. e n.$d24 cm 610 0 $aCiviltā cinese - Medicina alternativa - Tai Chi - Deambulazione 676 $a181 700 1$aChuckrow,$bRobert$0267031 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007388700403321 952 $a613.7148 CHUR 01$bBibl. 45004$fFLFBC 959 $aFLFBC 996 $aTai Chi walking$9692205 997 $aUNINA LEADER 05332nam 22008293 450 001 9910548277503321 005 20250628110046.0 010 $a3-030-67024-4 035 $a(CKB)5590000000896787 035 $a(MiAaPQ)EBC6893332 035 $a(Au-PeEL)EBL6893332 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/79344 035 $a(PPN)260826111 035 $a(ODN)ODN0010171413 035 $a(oapen)doab79344 035 $a(EXLCZ)995590000000896787 100 $a20220321d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMetalearning $eApplications to Automated Machine Learning and Data Mining 205 $a2nd ed. 210 $aCham$cSpringer Nature$d2022 210 1$aCham :$cSpringer International Publishing AG,$d2022. 210 4$dŠ2022. 215 $a1 online resource (349 pages) 225 1 $aCognitive Technologies 311 08$a3-030-67023-6 330 $aThis open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. 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