00887nam a2200277 i 450099100383959970753620030103110731.0020806s1999 it ||| | ita 8806145886b11872214-39ule_instPRUMB66204ExLDip.to SSSCita303.6Sofsky, Wolfgang143577Saggio sulla violenza /Wolfgang SofskyTorino :Einaudi,c1999196 p. ;22 cm.Saggi [Einaudi] ;819PotereViolenzaSaggi.b1187221428-04-1703-01-03991003839599707536LE021 DI9BISA2312021000161018le021-E0.00-ln 05050.i1212593303-01-03Traktat uber die Gewalt46677UNISALENTOle02101-01-02ma -itait 0100771nam a2200217 i 450099100233150970753620020507162718.0991020s1936 it ||| | ita b11641769-39ule_instLE02734696ExLDip.to Studi GiuridiciitaDe Martino, Francesco78850Lo Stato di Augusto :introduzione /Francesco De MartinoNapoli :G. Barca,1936154 p. ;25 cm..b1164176921-09-0602-07-02991002331509707536LE027 ARCHI M 3991LE027-5064le027-E0.00-l- 00000.i1186401102-07-02Stato di Augusto656094UNISALENTOle02701-01-99ma -itait 3103888nam 22005175 450 991074117500332120200701063121.03-319-70058-810.1007/978-3-319-70058-8(CKB)4100000001039725(DE-He213)978-3-319-70058-8(MiAaPQ)EBC5123299(PPN)221253289(EXLCZ)99410000000103972520171104d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierGranular Computing Based Machine Learning A Big Data Processing Approach /by Han Liu, Mihaela Cocea1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XV, 113 p. 27 illus., 19 illus. in color.) Studies in Big Data,2197-6503 ;353-319-70057-X Includes bibliographical references at the end of each chapters.This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data.     Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.Studies in Big Data,2197-6503 ;35Computational intelligenceBig dataComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Big Datahttps://scigraph.springernature.com/ontologies/product-market-codes/I29120Big Data/Analyticshttps://scigraph.springernature.com/ontologies/product-market-codes/522070Computational intelligence.Big data.Computational Intelligence.Big Data.Big Data/Analytics.006.3Liu Hanauthttp://id.loc.gov/vocabulary/relators/aut665835Cocea Mihaelaauthttp://id.loc.gov/vocabulary/relators/autBOOK9910741175003321Granular Computing Based Machine Learning3554353UNINA