LEADER 01647nam0 22003251i 450 001 SUN0034364 005 20050323120000.0 010 $a88-7750-312-2 100 $a20050323d1995 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aˆLa ‰critica d'arte del Novecento$fGianni Carlo Sciolla 210 $aTorino$cUtet$d1995 215 $aXIII, 416 p.$d24 cm. 606 $aCritica artistica$xSec. 20.$2FI$3SUNC015362 620 $dTorino$3SUNL000001 676 $a701.180904$v21 700 1$aSciolla$b, Gianni Carlo$3SUNV023231$037312 712 $aUTET$3SUNV000072$4650 801 $aIT$bSOL$c20181109$gRICA 912 $aSUN0034364 950 $aBIBLIOTECA DEL DIPARTIMENTO DI ARCHITETTURA E DISEGNO INDUSTRIALE$d01 PREST M $e01 5321 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI LETTERE E BENI CULTURALI$d07 CONS Kc 2000 bis $e07 9111 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI LETTERE E BENI CULTURALI$d07 CONS Kc 2000/I $e07 5102 995 $aBIBLIOTECA DEL DIPARTIMENTO DI ARCHITETTURA E DISEGNO INDUSTRIALE$bIT-CE0107$h5321$kPREST M$op$qa 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI LETTERE E BENI CULTURALI$bIT-CE0103$h9111$kCONS Kc 2000 bis$oc$qa 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI LETTERE E BENI CULTURALI$bIT-CE0103$h5102$kCONS Kc 2000/I$oc$qa 996 $aCritica d'arte del Novecento$998098 997 $aUNICAMPANIA LEADER 01644nam--2200481---450- 001 990000278450203316 005 20101214090120.0 010 $a88-14-08284-7 035 $a0027845 035 $aUSA010027845 035 $a(ALEPH)000027845USA01 035 $a0027845 100 $a20001018d2000----km-y0itay0103----ba 101 0 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aRimborsi spese e fringe benefits in azienda$ecompensi in natura a lavoratori dipendenti, rimborsi spese ...$fNevio Bianchi, Edoardo Cintolesi, Stefano Civitareale 205 $a2. ed 210 $aMilano$cGiuffráe$dc.2000 215 $aVII, 269 p.$d24 cm 225 2 $aDiritto e pratica fiscale 410 0$12001$aDiritto e pratica fiscale 606 $aRitenute fiscali$xGuide pratiche 606 $aStipendi e competenze accessorie$xTributi$xLegislazione 676 $a343.4505242 700 1$aBIANCHI,$bNevio$0541885 701 1$aCINTOLESI,$bEdoardo$036748 702 1$aCIVITAREALI,$bStefano 801 0$aIT$bsalbc$gISBD 912 $a990000278450203316 951 $aXXIV.5.C. 743 (IG VII 598)$b26368 G.$cXXIV.5.C. 743 (IG VII)$d00005802 959 $aBK 969 $aGIU 979 $aTAMI$b40$c20001018$lUSA01$h1613 979 $aTAMI$b40$c20001019$lUSA01$h0855 979 $c20001110$lUSA01$h1716 979 $aPATTY$b90$c20010521$lUSA01$h1741 979 $c20020403$lUSA01$h1636 979 $aPATRY$b90$c20040406$lUSA01$h1620 979 $aRSIAV5$b90$c20090902$lUSA01$h1424 979 $aRSIAV4$b90$c20101214$lUSA01$h0901 996 $aRimborsi spese e fringe benefits in azienda$9882002 997 $aUNISA LEADER 04859nam 2200481 450 001 9910484531203321 005 20220118121215.0 010 $a3-030-67073-2 024 7 $a10.1007/978-3-030-67073-3 035 $a(CKB)4100000011918847 035 $a(DE-He213)978-3-030-67073-3 035 $a(MiAaPQ)EBC6607162 035 $a(Au-PeEL)EBL6607162 035 $a(OCoLC)1250305589 035 $a(PPN)255883544 035 $a(EXLCZ)994100000011918847 100 $a20220118d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPatterns identification and data mining in weather and climate /$fAbdelwaheb Hannachi 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (XXIV, 600 p. 201 illus., 79 illus. in color.) 225 1 $aSpringer Atmospheric Sciences,$x2194-5217 311 $a3-030-67072-4 327 $aChapter 1 Introduction -- Chapter 2 General Setting and Basic Terminology -- Chapter 3 Empirical Orthogonal Functions -- Chapter 4 Rotated and Simplified EOFs -- Chapter 5 Complex/Hilbert EOFs -- Chapter 6 Principal Oscillation Patterns and their extension -- Chapter 7 Extended EOFs and SSA -- Chapter 8 Persistent, Predictive, and Interpolated Patterns -- Chapter 9 Principal Coordinates or Multidimensional Scaling -- Chapter 10 Factor Analysis -- Chapter 11 Projection Pursuit -- Chapter 12 Independent Component Analysis -- Chapter 13 Kernel EOFs -- Chapter 14 Functional and Regularised EOFs -- Chapter 15 Methods for Coupled Patterns -- Chapter 16 Further topics -- Chapter 17 Machine Learning -- Appendix A Smoothing Techniques -- Appendix B Introduction to Probability and Random Variables.-Appendix C Stationary Time Series Analysis.-Appendix D Matrix Algebra and Matrix Function -- Appendix E Optimisation Algorithms -- Appendix F Hilbert Space -- Appendix G Systems of Linear Ordinary Differential Equations -- Appendix H Links for Software Resource Material -- Index. 330 $aAdvances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K. 410 0$aSpringer Atmospheric Sciences,$x2194-5217 606 $aClimatology$xData processing 615 0$aClimatology$xData processing. 676 $a551.60285 700 $aHannachi$b Abdelwaheb$0996513 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484531203321 996 $aPatterns Identification and Data Mining in Weather and Climate$92284830 997 $aUNINA