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[null,"1.","Record Nr.",null,"UNINA9910437918803321"]
[null,null,"Titolo",null,"Minimum error entropy classification / / Joaquim P. Marques de Sa ... [et al.]"]
[null,null,"Pubbl/distr/stampa",null,"Berlin ; ; New York, : Springer, c2013"]
[null,null,"ISBN",null,"9783642290299"]
[{"$nulls":4},"3642290299"]
[null,null,"Edizione",null,"[1st ed. 2013.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVIII, 262 p.)"]
[null,null,"Collana",null,"Studies in computational intelligence, , 1860-949X ; ; 420"]
[null,null,"Disciplina",null,"006.3/1"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Computational learning 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","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications."]
[null,null,null,"Sommario/riassunto","This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions."]