LEADER 00863nam0-22002771i-450- 001 990003873810403321 035 $a000387381 035 $aFED01000387381 035 $a(Aleph)000387381FED01 035 $a000387381 100 $a19960715d--------km-y0itay50------ba 101 0 $aita 102 $aIT 200 1 $aShadow Activity and Unemployment in a Depressed Labor Market$fTito Boeri and Pietro Garibaldi 225 1 $aWorking Paper Series$fInnocenzo Gasparini Institute for Economic Research$v2000.177 676 $aG/2.12 700 1$aBoeri,$bTito$f<1958- >$039013 701 1$aGaribaldi,$bPietro$0146937 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003873810403321 952 $aPaper$fSES 959 $aSES 996 $aShadow Activity and Unemployment in a Depressed Labor Market$9516701 997 $aUNINA DB $aING01 LEADER 03713nam 22006975 450 001 9910739476403321 005 20200703231746.0 010 $a3-319-06938-1 024 7 $a10.1007/978-3-319-06938-8 035 $a(CKB)3710000000143823 035 $a(EBL)1782991 035 $a(SSID)ssj0001274419 035 $a(PQKBManifestationID)11710716 035 $a(PQKBTitleCode)TC0001274419 035 $a(PQKBWorkID)11325762 035 $a(PQKB)10765134 035 $a(DE-He213)978-3-319-06938-8 035 $a(MiAaPQ)EBC1782991 035 $a(PPN)179765590 035 $a(EXLCZ)993710000000143823 100 $a20140628d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning for Adaptive Many-Core Machines - A Practical Approach /$fby Noel Lopes, Bernardete Ribeiro 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (251 p.) 225 1 $aStudies in Big Data,$x2197-6503 ;$v7 300 $aIncludes index. 311 $a1-322-13600-9 311 $a3-319-06937-3 327 $aIntroduction -- Supervised Learning -- Unsupervised and Semi-supervised Learning -- Large-Scale Machine Learning. 330 $aThe overwhelming data produced everyday and the increasing performance and cost requirements of applications is transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together. 410 0$aStudies in Big Data,$x2197-6503 ;$v7 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aOperations research 606 $aDecision making 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aOperations research. 615 0$aDecision making. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aOperations Research/Decision Theory. 676 $a006.31 700 $aLopes$b Noel$4aut$4http://id.loc.gov/vocabulary/relators/aut$0739880 702 $aRibeiro$b Bernardete$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739476403321 996 $aMachine Learning for Adaptive Many-Core Machines - A Practical Approach$93553233 997 $aUNINA