LEADER 01510nam0 22003013i 450 001 SUN0097854 005 20140707101735.366 010 $a88-387-3240-X$d0.00 100 $a20140520d2005 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aˆIl ‰nuovo diritto d'autore$ela tutela della proprietà intellettuale nella società dell'informazione$eaggiornato alla riforma del diritto d'autore (L.31 marzo 2005, n. 43) e al codice dei diritti della proprietà industriale (D.Lgs. 10 febbraio 2005, n. 30)$fAndrea Sirotti Gaudenzi$gintroduzione di Patrizio Menchetti 205 $a3. ed 210 $aSantarcangelo di Romagna$cMaggioli$d2005 215 $a409 p.$d24 cm$e1 CD-ROM. - Fondo SSPL. 410 1$1001SUN0084834$12001 $aI *prontuari giuridici$fserie diretta da Andrea Sirotti Gaudenzi$v7$1210 $aSantarcangelo di Romagna$cMaggioli. 606 $aDiritto d'autore$2SG$3SUNC029773 620 $dSantarcangelo di Romagna$3SUNL000123 676 $a346.450482$v21 700 0$aSirotti Gaudenzi, Andrea$3SUNV069744$0263056 712 $aMaggioli$3SUNV000144$4650 801 $aIT$bSOL$c20181109$gRICA 912 $aSUN0097854 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA$d00 CONS SSPL.65 $e00 SPL84 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA$gSPL$h84$kCONS SSPL.65$op$qa 996 $aNuovo diritto d'autore$966574 997 $aUNICAMPANIA LEADER 03992nam 22008295 450 001 996198527203316 005 20230222232215.0 010 $a3-319-19222-1 024 7 $a10.1007/978-3-319-19222-2 035 $a(CKB)3710000000436857 035 $a(SSID)ssj0001547007 035 $a(PQKBManifestationID)16140827 035 $a(PQKBTitleCode)TC0001547007 035 $a(PQKBWorkID)14796335 035 $a(PQKB)10062711 035 $a(DE-He213)978-3-319-19222-2 035 $a(MiAaPQ)EBC6297155 035 $a(MiAaPQ)EBC5587750 035 $a(Au-PeEL)EBL5587750 035 $a(OCoLC)910936213 035 $a(PPN)186399251 035 $a(EXLCZ)993710000000436857 100 $a20150605d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in Computational Intelligence$b[electronic resource] $e13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part II /$fedited by Ignacio Rojas, Gonzalo Joya, Andreu Catala 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (XXV, 620 p. 187 illus.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v9095 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-19221-3 320 $aIncludes bibliographical references and index. 327 $aPattern recognition -- Embedded intelligent systems -- Expert systems -- Advances in computational intelligence.- Applications of computational intelligence. 330 $aThis two-volume set LNCS 9094 and LNCS 9095 constitutes the thoroughly refereed proceedings of the 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, held in Palma de Mallorca, Spain, in June 2013. The 99 revised full papers presented together with 1 invited talk were carefully reviewed and selected from 195 submissions. The papers are organized in topical sections on brain-computer interfaces: applications and tele-services; multi-robot systems: applications and theory (MRSAT); video and image processing; transfer learning; structures, algorithms and methods in artificial intelligence; interactive and cognitive environments; mathematical and theoretical methods in fuzzy systems; pattern recognition; embedded intelligent systems; expert systems; advances in computational intelligence; and applications of computational intelligence. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v9095 606 $aBioinformatics 606 $aPattern recognition systems 606 $aArtificial intelligence 606 $aData mining 606 $aComputer science 606 $aComputational and Systems Biology 606 $aAutomated Pattern Recognition 606 $aArtificial Intelligence 606 $aData Mining and Knowledge Discovery 606 $aModels of Computation 606 $aBioinformatics 615 0$aBioinformatics. 615 0$aPattern recognition systems. 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aComputer science. 615 14$aComputational and Systems Biology. 615 24$aAutomated Pattern Recognition. 615 24$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aModels of Computation. 615 24$aBioinformatics. 676 $a006.32 702 $aRojas$b Ignacio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJoya$b Gonzalo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCatala$b Andreu$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996198527203316 996 $aAdvances in Computational Intelligence$92045995 997 $aUNISA LEADER 04903nam 22006255 450 001 9910416083303321 005 20251113192307.0 010 $a3-030-45574-2 024 7 $a10.1007/978-3-030-45574-3 035 $a(CKB)4100000011372956 035 $a(DE-He213)978-3-030-45574-3 035 $a(MiAaPQ)EBC6284441 035 $a(Au-PeEL)EBL6284441 035 $a(OCoLC)1183957807 035 $a(PPN)260302899 035 $a(MiAaPQ)EBC30766836 035 $a(Au-PeEL)EBL30766836 035 $a(EXLCZ)994100000011372956 100 $a20200806d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGuide to Intelligent Data Science $eHow to Intelligently Make Use of Real Data /$fby Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo 205 $a2nd ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIII, 420 p. 179 illus., 122 illus. in color.) 225 1 $aTexts in Computer Science,$x1868-095X 311 08$a3-030-45573-4 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Practical Data Analysis: An Example -- Project Understanding -- Data Understanding -- Principles of Modeling -- Data Preparation -- Finding Patterns -- Finding Explanations -- Finding Predictors -- Evaluation and Deployment -- The Labelling Problem -- Appendix A: Statistics -- Appendix B: KNIME. 330 $aMaking use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: Guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring Includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix Provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms Integrates illustrations and case-study-style examples to support pedagogical exposition Supplies further tools and information at an associated website This practical and systematic textbook/reference is a ?need-to-have? tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover,it is a ?need to use, need to keep? resource following one's exploration of the subject. Prof. Dr. Michael R. Berthold is Professor for Bioinformatics and Information Mining at the University of Konstanz. Prof. Dr. Christian Borgelt is Professor for Data Science at the Paris Lodron University of Salzburg. Prof. Dr. Frank Höppner is Professor of Information Engineering at Ostfalia University of Applied Sciences. Prof. Dr. Frank Klawonn is Professor for Data Analysis and Pattern Recognition at the same institution and head of the Biostatistics Group at the Helmholtz Centre for Infection Research. Dr. Rosaria Silipo is a Principal Data Scientist and Head of Evangelism at KNIME AG. 410 0$aTexts in Computer Science,$x1868-095X 606 $aData mining 606 $aMachine learning 606 $aQuantitative research 606 $aData Mining and Knowledge Discovery 606 $aMachine Learning 606 $aData Analysis and Big Data 615 0$aData mining. 615 0$aMachine learning. 615 0$aQuantitative research. 615 14$aData Mining and Knowledge Discovery. 615 24$aMachine Learning. 615 24$aData Analysis and Big Data. 676 $a006.3 700 $aBerthold$b M$g(Michael),$0133096 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910416083303321 996 $aGuide to Intelligent Data Science$91891911 997 $aUNINA