00743nam0-22002531i-450-990001318680403321000131868FED01000131868(Aleph)000131868FED0100013186820000920d1990----km-y0itay50------baengChaos in Classical and Quantum MechanicsMartin C. Gutzwiller.New YorkSpringer-Verlag1990.Interdisciplinary Applied Mathematics1Gutzwiller,Martin C.47735ITUNINARICAUNIMARCBK990001318680403321121-M-268787MA1MA1Chaos in Classical and Quantum Mechanics354777UNINAING0100849nam a2200217 a 4500991002574149707536140730s 000 0 ara d9789981838284b1419692x-39ule_instBibl. Dip.le Aggr. Studi Umanistici - Sez. Filologia Linguistica e LetteraturaitaBerrada, Mohammed480029Al-Daw al-harib /Mohammed BerradaCasablanca:Nashr al-Fanak;1995198 pag. ;19 cm. Letteratura arabaRomanziSec. 20°..b1419692x05-08-1430-07-14991002574149707536LE008 FL.M. (Arabo) II A 31 12008000544836le008-E0.00-l- 00000.i1562860730-07-14Al-Daw al-harib257767UNISALENTOle00830-07-14ma -aramr 0003888nam 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