01027nam0-22003611i-450-99000075534040332120090331094739.088-207-1113-3000075534FED01000075534(Aleph)000075534FED0100007553420020821d1981----km-y0itay50------baitay-------001yySpettacolo e metropoliattore, messa in scena, spettatoreAlberto Abruzzese...[et al.]NapoliLiguoric1981144 p. [40] c. di tav.21 cmBibliotecaCultura e Mass-media5SpazioSpazio urbanoSpettacolo711701Abruzzese,Alberto<1942- >ITUNINARICAUNIMARCBK990000755340403321983 sez. Andriello983DARPUURB.LE B 556467FARBCFARBCDARPUSpettacolo e metropoli320621UNINA08010nam 2200733 450 991013748900332120220504234054.01-119-15365-41-119-14568-6(CKB)3710000000576507(SSID)ssj0001603975(PQKBManifestationID)16313641(PQKBTitleCode)TC0001603975(PQKBWorkID)14893934(PQKB)11636511(PQKBManifestationID)16314192(PQKB)20517385(DLC) 2015039877(Au-PeEL)EBL4334745(CaPaEBR)ebr11140521(CaONFJC)MIL888757(OCoLC)935920045(CaSebORM)9781119145677(MiAaPQ)EBC4334745(PPN)200812424(EXLCZ)99371000000057650720160122h20162016 uy 0engurcnu||||||||txtccrPredictive analytics the power to predict who will click, buy, lie, or die /Eric SiegelRevised and Updated Edition.Hoboken, New Jersey :Wiley,2016.©20161 online resource (338 pages) illustrations"Revised and updated."Includes index.1-119-17253-5 1-119-14567-8 Includes bibliographical references and index.Machine generated contents note: Foreword Thomas H. Davenport xiii Preface to the Revised and Updated Edition What's new and who's this book for--the Predictive Analytics FAQ Preface to the Original Edition xv What is the occupational hazard of predictive analytics? Introduction The Prediction Effect 1 How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die? Chapter 1 Liftoff! Prediction Takes Action (deployment) 17 How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system? Chapter 2 With Power Comes Responsibility: Hewlett-Packard, Target, the Cops, and the NSA Deduce Your Secrets (ethics) 37 How do we safely harness a predictive machine that can foresee job resignation, pregnancy, and crime? Are civil liberties at risk? Why does one leading health insurance company predict policyholder death? Two extended sidebars reveal: 1) Does the government undertake fraud detection more for its citizens or for self-preservation, and 2) for what compelling purpose does the NSA need your data even if you have no connection to crime whatsoever, and can the agency use machine learning supercomputers to fight terrorism without endangering human rights? Chapter 3 The Data E ffect: A Glut at the End of the Rainbow (data) 67 We are up to our ears in data. How much can this raw material really tell us? What actually makes it predictive? What are the most bizarre discoveries from data? When we find an interesting insight, why are we often better off not asking why? In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? Chapter 4 The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling) 103 What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine's predictions? Why couldn't prediction prevent the global financial crisis? Chapter 5 The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) 133 To crowdsource predictive analytics--outsource it to the public at large--a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds? Chapter 6 Watson and the Jeopardy! Challenge (question answering) 151 How does Watson--IBM's Jeopardy!-playing computer--work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone's Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible? Chapter 7 Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) 187 What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived, and that you can't even be sure has happened afterward--but that can be predicted in advance? Afterword 218 Eleven Predictions for the First Hour of 2022 Appendices A. The Five Effects of Prediction 221 B. Twenty Applications of Predictive Analytics 222 C. Prediction People--Cast of "Characters" 225 Notes 228 Acknowledgments 290 About the Author 292 Index 293 ."Predictive analytics unleashes the power of data. With this technology, computers literally learn from data how to predict future behaviors of individuals. In this updated and revised edition of Predictive Analytics, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction. New material includes: - The Real Reason the NSA Wants Your Data: Automatic Suspect Discovery. A special sidebar in Chapter 2, "With Power Comes Responsibility," presumes--with much evidence--that the National Security Agency considers PA a strategic priority. Can the organization use PA without endangering civil liberties? - Dozens of new examples from Facebook, Hopper, Shell, Uber, UPS, the U.S. government, and more. The Central Tables' compendium of mini-case studies has grown to 182 entries, including breaking examples. - A much needed warning regarding bad science. Chapter 3, "The Data Effect," includes an in-depth section about an all-too-common pitfall, and how we avoid it, i.e., how to successfully tap data's potential without being fooled by random noise, ensuring sound discoveries are made. - Even more extensive Notes, updated and expanded to 70+ pages, now moved to an online PDF. Now located at www.predictivenotes.com, the Notes include citations and comments that cover the above new content, as well as new citations for many other topics"--Provided by publisher.Social sciencesForecastingPrediction (Psychology)Economic forecastingSocial predictionHuman behaviorSocial sciencesForecasting.Prediction (Psychology)Economic forecasting.Social prediction.Human behavior.303.49BUS016000BUS021000BUS043000bisacshSiegel Eric1968-915329MiAaPQMiAaPQMiAaPQBOOK9910137489003321Predictive analytics2051673UNINA01442nam0 22003131i 450 UON0028449420231205103854.79584-931118-0-520061127d1999 |0itac50 baspaSYRES||||e |||||Breve diccionario sirìacoSirìaco - Castellano - CatalanPor Joan Ferrer y Maria Antònia NoguerasBarcelonaUniversitat de Barcelona - Area d'Estudios Hebreus i Arameus1999V, 324 p.21 cmFondi FIRBIT-UONSI SIPAII SRA/011 N001UON002571892001 Estudios de Filologìa SemiticaDirector de la colecciòn y editor Josep Ribera-Florit1LINGUA SIRIACADIZIONARIUONC002573FIESBarcelonaUONL003004SIPA II SR ASIRIA E PALESTINA - LINGUISTICA - SIRIACO - DIZIONARIAFERRERJoanUONV164316693111NOGUERASMaria AntoniaUONV164318693112Universitat de Barcelona. Area d'Estudis Hebreus i ArameusUONV270367650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00284494SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI SIPA II SR A 011 N SI SA 118732 7 011 N Fondi FIRBBreve diccionario sirìaco1245544UNIOR05061nas1 22008173i 450 CFI057258220251003044144.0IT2004-123S 20150605a20049999||||0itac50 baitaita|u||||||||z01i xxxe z01nz01ncRDAcarrierOspedali & salute... rapporto annualeIlesis associazione italiana ospedalità privata, Ermenia studi & strategie di sistemaN. 1 (2003)- MilanoF. Angeli2004-volumi23 cmCollana AIOPAssociazione italiana ospedalità privataAnnualeDal 2004 il curatore varia: ErmeneiaDal 2017 scaricabile gratuitamente dalla piattaforma Open Access di FrancoAngeliAltro titolo da 7. (2009): ˆIl ‰sistema sanitario della LombardiaBVE0741596001CFI05725862001 Collana AIOPAssociazione italiana ospedalità privata71202AIOPBVEV051023001ANA04957322001 Ospedali & salutedodicesimo rapporto annuale 2014Ermeneia, Studi & Strategie di Sistema001BAS02220842001 Ospedali & Salutesesto rapporto annuale 20086001BAS02387572001 Ospedali & salutequinto rapporto annuale, 2007Ermeneia001BAS02398372001 Ospedali & salutesettimo rapporto annuale 20097001BAS02437002001 Ospedali & saluteottavo rapporto annuale 2010[a cura di Nadio Delai], Ermeneia Studi & strategie di sistema001BAS02632312001 Ospedali & saluteundicesimo rapporto annuale 2013001BAS02683342001 Ospedali & salutedodicesimo rapporto annuale 2014001BAS02838572001 Ospedali & salutesedicesimo rapporto annuale 2018Ermeneia, Studi & Strategie di Sistema[a cura di Nadio Delai]16001BAS02857802001 Ospedali & salutediciassettesimo rapporto annuale 2019Ermeneia, Studi & Strategie di Sistema001RML01616262001 Ospedali & saluteterzo rapporto annuale 20053001SBT00391322001 Ospedali & salutediciannovesimo rapporto annuale 2021Ermeneia, Studi & Strategie[a cura di Nadio Delai]001SNT00064412001 Ospedali & salutequarto rapporto annuale, 2006Ermeneia001TER00097042001 Ospedali & salutesecondo rapporto annuale 2004001UBO33693432001 Ospedali & salutequinto rapporto annuale 2007Ermeneia001UBO39179442001 Ospedali & salutenono rapporto annuale 2011Ermeneia001UBO41674642001 Ospedali & salutetredicesimo rapporto annuale 201513001UBO42306472001 Ospedali & salutequattordicesimo rapporto annuale 201614001UBO45584662001 Ospedali & salutediciottesimo rapporto annuale 2020Ermeneia, Studi & Strategie di Sistema001UBO46989342001 Ospedali & saluteventesimo rapporto annuale 2022Ermeneia, Studi & Strategie[a cura di Nadio Delai]001UBO47947482001 Ospedali & saluteventunesimo rapporto annuale 2023Censis001UBS00089882001 Ospedali & salutequindicesimo rapporto annuale 2017Ermeneia, Studi & strategie di sistema[a cura di Nadio Delai]15001VEA10934252001 Ospedali & salutedecimo rapporto annuale 2012ˆIl ‰sistema sanitario della LombardiaBVE0741596Ospedali e saluteCFI0572584362.1PROBLEMI E SERVIZI SOCIALI. MALATTIE FISICHE19362.1105PROBLEMI E SERVIZI DI ASSISTENZA SOCIALE. OSPEDALI E ISTITUTI AFFINI. SERIALI21362.110945SERVIZI DI OSPEDALI E ISTITUTI AFFINI. ITALIA20362.110945PROBLEMI E SERVIZI DI ASSISTENZA SOCIALE. OSPEDALI E ISTITUTI AFFINI. Italia21WX 158HAIOPBVEV051023Ilesis <società>CFIV201519Ermeneia <società>CFIV215226Associazione italiana ospedalità privataCFIV220842AIOPAssociazione italiana ospedalità privataCFIV220842AIOPErmeneia, studi & strategie di sistemaCFIV273873Ermeneia <società>Ermeneia, studi & strategie di sistemaCFIV273873Ermeneia <società>ITIT-00000020150605IT-NA0079 NAP BNMAGAZZINO magazzini correnti divisi per anniNAP 01POZZO LIB.Vi sono collocati fondi di economia, periodici di ingegneria e scienze, periodici di economia e statistica e altri fondi comprendenti documenti di economia pervenuti in dono. CFI0572582Biblioteca Centralizzata di Ateneo1 v. 01POZZO LIB.F. BENCARDINO 23422 01 BNOspedali & salute1408488UNISANNIO