LEADER 01112cam0 2200265 450 001 E600200033898 005 20200422153126.0 100 $a20080131d1973 |||||ita|0103 ba 101 $aita 102 $aIT 200 1 $aRotoli di exultet dell'Italia meridionale$eexultet 1, 2 benedizionale dell'archivio della cattedrale di Bari$e exultet 1, 2, 3 dell'archivio capitolare di Troia$fGuglielmo Cavallo$gcontributio sull'exultet 3 di Troia di Carlo Bertelli$gprefazione di Armando Petrucci 210 $aBari$cAdriatica editrice$d1973 215 $aXV, 262 p., tav.$cill.$d34 cm 700 1$aCavallo$b, Guglielmo$3AF00009927$4070$037688 702 1$aBertelli, Carlo$3AF00009815$4070 702 1$aPetrucci, Armando$3AF00003697$4070 801 0$aIT$bUNISOB$c20200422$gRICA 850 $aUNISOB 852 $aUNISOB$j010$m17654 912 $aE600200033898 940 $aM 102 Monografia moderna SBN 941 $aM 957 $a010$b000697$gSi$d17654$racquisto$1pomicino$2UNISOB$3UNISOB$420080131131948.0$520200422153009.0$6Alfano 996 $aRotoli di exultet dell'Italia meridionale$9234405 997 $aUNISOB LEADER 03139nam 22004935 450 001 9910299577103321 005 20251113183101.0 010 $a3-319-73040-1 024 7 $a10.1007/978-3-319-73040-0 035 $a(CKB)4100000001794713 035 $a(DE-He213)978-3-319-73040-0 035 $a(MiAaPQ)EBC5231082 035 $a(PPN)223956317 035 $a(EXLCZ)994100000001794713 100 $a20180118d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aVisual Knowledge Discovery and Machine Learning /$fby Boris Kovalerchuk 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XXI, 317 p. 274 illus., 263 illus. in color.) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v144 311 08$a3-319-73039-8 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aMotivation, Problems and Approach -- General Line Coordinates (GLC) -- Theoretical and Mathematical Basis of GLC -- Adjustable GLCs for decreasing occlusion and pattern simplification -- GLC Case Studies -- Discovering visual features and shape perception capabilities in GLC -- Interactive Visual Classification, Clustering and Dimension Reduction with GLC-L -- Knowledge Discovery and Machine Learning for Investment Strategy with CPC. 330 $aThis book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science. 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v144 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aKovalerchuk$b Boris$4aut$4http://id.loc.gov/vocabulary/relators/aut$0846538 906 $aBOOK 912 $a9910299577103321 996 $aVisual Knowledge Discovery and Machine Learning$92542748 997 $aUNINA