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[null,"1.","Record Nr.",null,"UNISALENTO991000353249707536"]
[null,null,"Autore",null,"Luerti, Angelo"]
[null,null,"Titolo",null,"Il piano dei conti generale : riferimenti normativi, ministeriali e commenti per ogni voce / Angelo Luerti"]
[null,null,"Pubbl/distr/stampa",null,"[Milano] : Etaslibri, 1995"]
[null,null,"ISBN",null,"9788845307393"]
[null,null,"Descrizione fisica",null,"128 p. ; 24 cm"]
[null,null,"Collana",null,"Gestione d'impresa. Contabilità e controllo di gestione"]
[null,null,"Disciplina",null,"657.3"]
[null,null,"Soggetti",null,"Contabilità"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISALENTO991000452159707536"]
[null,null,"Autore",null,"Sciarra, Silvana"]
[null,null,"Titolo",null,"Contratto collettivo e contrattazione in azienda / Silvana Sciarra"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Angeli, c1985"]
[null,null,"Descrizione fisica",null,"226 p. ; 22 cm"]
[null,null,"Collana",null,"Collana di diritto del lavoro ; 32"]
[null,null,"Disciplina",null,"344.450189"]
[null,null,"Soggetti",null,"Contratti collettivi e sindacati"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"3.","Record Nr.",null,"UNISALENTO991002205849707536"]
[null,null,"Autore",null,"Bonfatti, Sido"]
[null,null,"Titolo",null,"Procedure concorsuali e problemi della prassi : atti del Convegno svoltosi in Lanciano presso la Banca popolare di Lanciano e Sulmona i giorni 31 maggio e 1 giugno 1996 / a cura di Sido Bonfatti e Giovanni Falcone"]
[null,null,"Pubbl/distr/stampa",null,"Milano : A. Giuffrè, [1997]"]
[null,null,"ISBN",null,"8814065276"]
[null,null,"Descrizione fisica",null,"viii, 153 p. ; 24 cm."]
[null,null,"Classificazione",null,"CM-VIII/A"]
[null,null,"Disciplina",null,"346.450780269"]
[null,null,"Soggetti",null,"Procedure concorsuali - Congressi"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"4.","Record Nr.",null,"UNINA9910739403203321"]
[null,null,"Autore",null,"Kramer Oliver"]
[null,null,"Titolo",null,"Machine Learning for Evolution Strategies / / by Oliver Kramer"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016"]
[null,null,"ISBN",null,"3-319-33383-6"]
[null,null,"Edizione",null,"[1st ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (IX, 124 p. 38 illus. in color.)"]
[null,null,"Collana",null,"Studies in Big Data, , 2197-6511 ; ; 20"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Computer simulation"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"System theory"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Computer Modelling"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Complex Systems"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references at the end of each chapters and index."]
[null,null,"Nota di contenuto",null,"Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning."]
[null,null,null,"Sommario/riassunto","This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research."]