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[null,"1.","Record Nr.",null,"UNINA990004326600403321"]
[null,null,"Autore",null,"Guidobaldi, Maria Paola"]
[null,null,"Titolo",null,"Musica e danza / Maria Paola Guidobaldi"]
[null,null,"Pubbl/distr/stampa",null,"Roma : Edizioni Quasar, 1992"]
[null,null,"ISBN",null,"88-7097-024-8"]
[null,null,"Descrizione fisica",null,"86 p. : ill. ; 25 cm"]
[null,null,"Collana",null,"Vita e costumi dei romani antichi ; 13"]
[null,null,"Disciplina",null,"780.937"]
[null,null,"Locazione",null,"FLFBC"]
[{"$nulls":4},"DDR"]
[null,null,"Collocazione",null,"780.937 GUID 1"]
[{"$nulls":4},"DDR-XXVII B 009 (13)"]
[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,"UNINA9910438057103321"]
[null,null,"Autore",null,"Kramer Oliver"]
[null,null,"Titolo",null,"Dimensionality Reduction with Unsupervised Nearest Neighbors / / by Oliver Kramer"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013"]
[null,null,"ISBN",null,"9783642386527"]
[{"$nulls":4},"3642386520"]
[null,null,"Edizione",null,"[1st ed. 2013.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xviii, 130 pages) : illustrations (some color)"]
[null,null,"Collana",null,"Intelligent Systems Reference Library, , 1868-4408 ; ; 51"]
[null,null,"Disciplina",null,"006.31"]
[{"$nulls":4},"519.5/36"]
[null,null,"Soggetti",null,"Engineering mathematics"]
[{"$nulls":4},"Engineering - Data processing"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Operations research"]
[{"$nulls":4},"Mathematical and Computational Engineering Applications"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Operations Research and Decision Theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"ISSN: 1868-4394.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions."]
[null,null,null,"Sommario/riassunto","This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to "]
[{"$nulls":4},"illustrate the introduced concepts and to highlight the experimental results.  ."]