LEADER 02337nam 2200649 a 450 001 9910464843503321 005 20170924212847.0 010 $a1-4623-4777-0 010 $a1-4527-7939-2 010 $a1-283-45039-9 010 $a9786613823663 010 $a1-4519-1010-X 035 $a(CKB)3360000000443362 035 $a(EBL)3012548 035 $a(SSID)ssj0001323906 035 $a(PQKBManifestationID)11978484 035 $a(PQKBTitleCode)TC0001323906 035 $a(PQKBWorkID)11505603 035 $a(PQKB)10456336 035 $a(OCoLC)568151313 035 $a(MiAaPQ)EBC3012548 035 $a(EXLCZ)993360000000443362 100 $a20090811d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBank risk-taking and competition revisited$b[electronic resource] $enew theory and new evidence /$fprepared by John H. Boyd, Gianni De Nicolo?, and Abu M. Jalal 210 $a[Washington, D.C.] $cInternational Monetary Fund$d2006 215 $a1 online resource (51 p.) 225 1 $aIMF working paper ;$vWP/06/297 300 $a"December 2006." 311 $a1-4518-6557-0 320 $aIncludes bibliographical references (p. 48-49). 327 $a""Bank Risk-Taking and Competition Revisited: New Theory and New Evidence""; ""Contents""; ""I. INTRODUCTION""; ""II. THEORY""; ""III. EVIDENCE""; ""IV. CONCLUSION""; ""Appendix I. Pareto Dominant Equilibria""; ""References"" 410 0$aIMF working paper ;$vWP/06/297. 606 $aBank failures$xEconometric models 606 $aCompetition$xEconometric models 606 $aBank loans$xEconometric models 606 $aRisk$xEconometric models 608 $aElectronic books. 615 0$aBank failures$xEconometric models. 615 0$aCompetition$xEconometric models. 615 0$aBank loans$xEconometric models. 615 0$aRisk$xEconometric models. 700 $aBoyd$b John Harvey$f1953-$0990400 701 $aDe Nicolo?$b Gianni$0375199 701 $aJalal$b Abu M$0990401 712 02$aInternational Monetary Fund.$bResearch Dept. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910464843503321 996 $aBank risk-taking and competition revisited$92265771 997 $aUNINA LEADER 04598nam 2200769 450 001 9910260628303321 005 20230829002916.0 010 $a0-585-34101-X 035 $a(CKB)111004366547760 035 $a(MH)007132931-5 035 $a(SSID)ssj0000248526 035 $a(PQKBManifestationID)12044489 035 $a(PQKBTitleCode)TC0000248526 035 $a(PQKBWorkID)10202427 035 $a(PQKB)10161793 035 $a(SSID)ssj0000937693 035 $a(PQKBManifestationID)11502509 035 $a(PQKBTitleCode)TC0000937693 035 $a(PQKBWorkID)10877598 035 $a(PQKB)10252099 035 $a(CaBNVSL)mat06308075 035 $a(IDAMS)0b0000648190889b 035 $a(IEEE)6308075 035 $a(WaSeSS)Ind00066057 035 $a(EXLCZ)99111004366547760 100 $a20151223d2003 uy 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSolving problems in environmental engineering and geosciences with artificial neural networks /$fFarid U. Dowla and Leah L. Rogers 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2003] 210 1$aCambridge, Massachusetts :$cMIT Press,$dc1995. 215 $a1 online resource (x, 239 p. )$cill., maps ; 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-262-27191-5 311 $a0-262-04148-0 320 $aIncludes bibliographical references and index. 330 $aArtificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. 606 $aEarth sciences$xData processing 606 $aEnvironmental engineering$xData processing 606 $aNeural networks (Computer science) 606 $aEarth sciences$xData processing 606 $aEnvironmental engineering$xData processing 606 $aGeology - General$2HILCC 606 $aGeology$2HILCC 606 $aEarth & Environmental Sciences$2HILCC 615 0$aEarth sciences$xData processing. 615 0$aEnvironmental engineering$xData processing. 615 0$aNeural networks (Computer science) 615 0$aEarth sciences$xData processing 615 0$aEnvironmental engineering$xData processing 615 7$aGeology - General 615 7$aGeology 615 7$aEarth & Environmental Sciences 676 $a550/.285 700 $aDowla$b Farid U.$0731085 701 $aRogers$b Leah L$01208148 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910260628303321 996 $aSolving problems in environmental engineering and geosciences with artificial neural networks$92787192 997 $aUNINA 999 $aThis Record contains information from the Harvard Library Bibliographic Dataset, which is provided by the Harvard Library under its Bibliographic Dataset Use Terms and includes data made available by, among others the Library of Congress