LEADER 01063nam0 2200265 450 001 000036250 005 20140326094926.0 100 $a20140312d1925----km-y0itaa50------ba 101 0 $aita 102 $aIT 200 1 $aCome vivono gli animali$ecompendio di morfologia e biologia animale ad uso delle scuole medie superiori$fLino Vaccari 210 $aTorino ; Genova$cS. Lattes$d1925 215 $aVIII, 479 p.$cill.$d24 cm. 316 $aSu una delle pagine precedenti il front.: Gioacchino Viggiani 25 luglio 1927 606 1 $aAnimali 676 $a590$v(22. ed.)$9Animali 700 1$aVaccari,$bLino$0446803 801 0$aIT$bUniversità della Basilicata - B.I.A.$gREICAT$2unimarc 912 $a000036250 996 $aCome vivono gli animali$9101548 997 $aUNIBAS CAT $aSTD097$b01$c20140312$lBAS01$h1359 CAT $aTTM$b30$c20140326$lBAS01$h0949 FMT Z30 -1$lBAS01$LBAS01$mBOOK$1BASA2$APolo Tecnico-Scientifico$2FVIG$BFondo Viggiani$3FVig/41416$641416$5T41416$7Collocato presso la Scuola di Agraria$820140312$f35$FStanza riservata LEADER 04147nam 2200685 450 001 9910260629203321 005 20200520144314.0 010 $a1-280-67835-6 010 $a9786613655288 010 $a0-262-30118-0 035 $a(CKB)2560000000082843 035 $a(OCoLC)794669892 035 $a(CaPaEBR)ebrary10569012 035 $a(SSID)ssj0000681124 035 $a(PQKBManifestationID)11390235 035 $a(PQKBTitleCode)TC0000681124 035 $a(PQKBWorkID)10655163 035 $a(PQKB)10302005 035 $a(MiAaPQ)EBC3339451 035 $a(CaBNVSL)mat06267536 035 $a(IDAMS)0b000064818b458c 035 $a(IEEE)6267536 035 $a(PPN)180003445 035 $a(Au-PeEL)EBL3339451 035 $a(CaPaEBR)ebr10569012 035 $a(CaONFJC)MIL365528 035 $a(EXLCZ)992560000000082843 100 $a20151223d2012 uy 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBoosting $efoundations and algorithms /$fRobert E. Schapire and Yoav Freund 210 1$aCambridge, Massachusetts :$cMIT Press,$dc2012. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2012] 215 $a1 online resource (544 p.) 225 1 $aAdaptive computation and machine learning series 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-262-52603-4 311 $a0-262-01718-0 320 $aIncludes bibliographical references and indexes. 327 $aFoundations of machine learning -- Using AdaBoost to minimize training error -- Direct bounds on the generalization error -- The margins explanation for boosting's effectiveness -- Game theory, online learning, and boosting -- Loss minimization and generalizations of boosting -- Boosting, convex optimization, and information geometry -- Using confidence-rated weak predictions -- Multiclass classification problems -- Learning to rank -- Attaining the best possible accuracy -- Optimally efficient boosting -- Boosting in continuous time. 330 $aBoosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout. 410 0$aAdaptive computation and machine learning 606 $aBoosting (Algorithms) 606 $aSupervised learning (Machine learning) 608 $aElectronic books. 615 0$aBoosting (Algorithms) 615 0$aSupervised learning (Machine learning) 676 $a006.3/1 700 $aSchapire$b Robert E.$01052168 701 $aFreund$b Yoav$0999237 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910260629203321 996 $aBoosting$92483176 997 $aUNINA LEADER 02775nam 2200433 450 001 9910467521203321 005 20200124195036.0 010 $a93-5280-367-1 035 $a(CKB)4100000007176438 035 $a(MiAaPQ)EBC5323988 035 $a(Au-PeEL)EBL5323988 035 $a(CaPaEBR)ebr11637712 035 $a(OCoLC)985602192 035 $a(EXLCZ)994100000007176438 100 $a20200124d2017 uy 0 101 0 $ahin 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMaanav Taskari se Sangharsh $eNiti or Kanoon me Kamiyan /$fVeerendra Mishra 210 1$aLos Angeles ;$aLondon ;$aNew Delhi, India :$cSage Bhasha,$d2017. 215 $a1 online resource (321 pages) 300 $aHuman Trafficking: Understanding its trends, dimensions, and gaps in policy and law that need to be plugged. 311 $a93-85985-57-4 327 $aPreface; Acknowledgments; Revisiting Definition of Human Trafficking; Diverse Perspectives to Combat Human Trafficking; Broadening Dimensions of Human Trafficking; Commercial Sexual Exploitation; Labor Trafficking and Other Dimensions; Dynamics of Cause and Effect: Challenge to Social Justice System; Gaps in Law Enforcement: Challenge to Criminal Justice System; Multiple Agency Approach and Partnership; Wayward Justice: Brute Mute Theory; Socio-criminal Legislations: A New Dimension to Criminal Justice System; Waiting for Ethical Justice: Case of Bedia Community and Native Americans; The Way Forward: Recommendations; Index. 330 $aThis book demystifies the term "trafficking" with a view to properly understand its trends, dimensions, and gaps in policy and law that need to be plugged. Combating Human Trafficking aims to initiate fresh discussion on human trafficking, and offers recommendations to curb organized international crime. It explores varied dimensions of the crime and offers further classification to help effectively address the problem. It presents a new perspective of identifying assimilative interaction between social and criminal justice systems, the progressive growth in socio-criminal legislations, and the universal demand of multi-agency approach to combat trafficking. Through the Brute Mute theory, it gives an illustrative description of micro- and macro-governance, and offers a global perspective to the problem with examples and case studies. 606 $aSocial sciences 608 $aElectronic books. 615 0$aSocial sciences. 676 $a300.8 700 $aMishra$b Veerendra$0934173 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910467521203321 996 $aMaanav Taskari se Sangharsh$92103157 997 $aUNINA LEADER 01394nam 2200361Ka 450 001 9910697789203321 005 20081027141045.0 035 $a(CKB)5470000002391077 035 $a(OCoLC)263685435 035 $a(EXLCZ)995470000002391077 100 $a20081027d2001 ua 0 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalytical versus numerical estimates of water-level declines caused by pumping, and a case study of the Iao Aquifer, Maui, Hawaii$b[electronic resource] /$fby Delwyn S. Oki and William Meyer 210 1$aHonolulu, Hawaii :$cU.S. Dept. of the Interior, U.S. Geological Survey,$d2001. 215 $aiv, 31 pages $cdigital, PDF file 225 1 $aWater-resources investigations report ;$v00-4244 300 $aTitle from title screen (viewed Oct. 27, 2008). 606 $aWater table$zHawaii$zMaui 606 $aWater withdrawals$zHawaii$zMaui 615 0$aWater table 615 0$aWater withdrawals 700 $aOki$b Delwyn S$01384236 701 $aMeyer$b William$f1939-$01385935 712 02$aGeological Survey (U.S.) 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910697789203321 996 $aAnalytical versus numerical estimates of water-level declines caused by pumping, and a case study of the Iao Aquifer, Maui, Hawaii$93434396 997 $aUNINA