01282nam0 2200301 i 450 SUN000463520120314030821.713IT89 629820020724g1988 |0itac50 baitaIT|||| |||||Arbitrati e modelli arbitrali nel diritto amministrativoGiuseppe CaiaEd. provvisoriaBolognaConti1989VIII, 225 p.25 cm.001SUN00037292001 Seminario giuridico della Università di Bologna130210 MilanoGiuffrè[poi]BolognaBononia university.ArbitratoDiritto amministrativoFISUNC002901BolognaSUNL000003342.45066Procedimento amministrativo. Italia21Caia, GiuseppeSUNV00110139216ContiSUNV003561650ITSOL20181109RICASUN0004635UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00 CONS IV.Et.7 00 594 UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA594CONS IV.Et.7paArbitrati e modelli arbitrali nel diritto amministrativo873994UNICAMPANIA04147nam 2200685 450 991026062920332120200520144314.01-280-67835-697866136552880-262-30118-0(CKB)2560000000082843(OCoLC)794669892(CaPaEBR)ebrary10569012(SSID)ssj0000681124(PQKBManifestationID)11390235(PQKBTitleCode)TC0000681124(PQKBWorkID)10655163(PQKB)10302005(MiAaPQ)EBC3339451(CaBNVSL)mat06267536(IDAMS)0b000064818b458c(IEEE)6267536(PPN)180003445(Au-PeEL)EBL3339451(CaPaEBR)ebr10569012(CaONFJC)MIL365528(EXLCZ)99256000000008284320151223d2012 uy engurcn|||||||||txtccrBoosting foundations and algorithms /Robert E. Schapire and Yoav FreundCambridge, Massachusetts :MIT Press,c2012.[Piscataqay, New Jersey] :IEEE Xplore,[2012]1 online resource (544 p.) Adaptive computation and machine learning seriesBibliographic Level Mode of Issuance: Monograph0-262-52603-4 0-262-01718-0 Includes bibliographical references and indexes.Foundations of machine learning -- Using AdaBoost to minimize training error -- Direct bounds on the generalization error -- The margins explanation for boosting's effectiveness -- Game theory, online learning, and boosting -- Loss minimization and generalizations of boosting -- Boosting, convex optimization, and information geometry -- Using confidence-rated weak predictions -- Multiclass classification problems -- Learning to rank -- Attaining the best possible accuracy -- Optimally efficient boosting -- Boosting in continuous time.Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.Adaptive computation and machine learningBoosting (Algorithms)Supervised learning (Machine learning)Electronic books.Boosting (Algorithms)Supervised learning (Machine learning)006.3/1Schapire Robert E.1052168Freund Yoav999237CaBNVSLCaBNVSLCaBNVSLBOOK9910260629203321Boosting2483176UNINA