LEADER 01544nam a2200313 i 4500 001 991002397129707536 005 20020508195525.0 008 990114s1964 it 00| 0 ita d 035 $ab11003133-39ule_inst 035 $aPARLA161770$9ExL 040 $aDip.to Filosofia$bita 082 0 $a791.45 100 1 $aDe Rita, Lidia$0120382 245 12$aI contadini e la televisione :$bstudio sull'influenza degli spettacoli televisivi in un gruppo di contadini lucani /$cLidia De Rita 260 $aBologna :$bIl mulino,$c1964 300 $aVIII, 307 p. :$bill. ;$c22 cm 490 0 $aStudi e Ricerche / il Mulino ;$v2 650 4$aContadini$xBasilicata$xInchieste sociologiche 650 4$aTrasmissioni televisive$xItalia 907 $a.b11003133$b23-02-17$c28-06-02 912 $a991002397129707536 945 $aLE005 300 DER02. 01$g1$i2005000038143$lle005$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11119743$z28-06-02 945 $aLE002 Ps. III D 22$g1$i2002000559470$lle002$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10397450$z27-06-02 945 $aLE022 MPs-S 115 A 12$g1$i2022000171069$lle022$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i13882533$z27-09-04 945 $aLE022 MP 67 B 1$g2$i2022000045667$lle022$nS.i.$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i13920510$z10-11-04 945 $aLE009 STOR.68-16$g1$i2009000393547$lle009$o-$pE0.00$q-$rl$s- $t0$u2$v0$w2$x0$y.i13031880$z25-11-03 996 $aContadini e la televisione$9860937 997 $aUNISALENTO 998 $ale005$ale002$a(2)le022$ale009$b01-01-99$cm$da $e-$fita$git $h2$i5 LEADER 03526nam 22006615 450 001 9910349319503321 005 20220317143448.0 010 $a3-030-24494-6 024 7 $a10.1007/978-3-030-24494-1 035 $a(CKB)4100000009374873 035 $a(DE-He213)978-3-030-24494-1 035 $a(MiAaPQ)EBC5905199 035 $a(PPN)25886320X 035 $a(EXLCZ)994100000009374873 100 $a20190925d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian Optimization and Data Science /$fby Francesco Archetti, Antonio Candelieri 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XIII, 126 p. 52 illus., 39 illus. in color.) 225 1 $aSpringerBriefs in Optimization,$x2190-8354 311 $a3-030-24493-8 327 $a1. Automated Machine Learning and Bayesian Optimization -- 2. From Global Optimization to Optimal Learning -- 3. The Surrogate Model -- 4. The Acquisition Function -- 5. Exotic BO -- 6. Software Resources -- 7. Selected Applications. 330 $aThis volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities. 410 0$aSpringerBriefs in Optimization,$x2190-8354 606 $aOperations research 606 $aManagement science 606 $aMachine learning 606 $aComputer software 606 $aStatistics 606 $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aMathematical Software$3https://scigraph.springernature.com/ontologies/product-market-codes/M14042 606 $aBayesian Inference$3https://scigraph.springernature.com/ontologies/product-market-codes/S18000 615 0$aOperations research. 615 0$aManagement science. 615 0$aMachine learning. 615 0$aComputer software. 615 0$aStatistics. 615 14$aOperations Research, Management Science. 615 24$aMachine Learning. 615 24$aMathematical Software. 615 24$aBayesian Inference. 676 $a519.6 676 $a519.542 700 $aArchetti$b Francesco$4aut$4http://id.loc.gov/vocabulary/relators/aut$060966 702 $aCandelieri$b Antonio$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349319503321 996 $aBayesian Optimization and Data Science$92498803 997 $aUNINA