02695nam 2200601Ia 450 991043791880332120200520144314.09783642290299364229029910.1007/978-3-642-29029-9(CKB)3390000000030171(SSID)ssj0000746044(PQKBManifestationID)11489085(PQKBTitleCode)TC0000746044(PQKBWorkID)10862915(PQKB)10689108(DE-He213)978-3-642-29029-9(MiAaPQ)EBC3070954(PPN)168313472(EXLCZ)99339000000003017120120803d2013 uy 0engurnn|008mamaatxtccrMinimum error entropy classification /Joaquim P. Marques de Sa ... [et al.]1st ed. 2013.Berlin ;New York Springerc20131 online resource (XVIII, 262 p.) Studies in computational intelligence,1860-949X ;420Bibliographic Level Mode of Issuance: Monograph9783642437427 3642437427 9783642290282 3642290280 Includes bibliographical references and index.Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications.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.Studies in computational intelligence ;v. 420.Machine learningComputational learning theoryMachine learning.Computational learning theory.006.3/1Sa J. P. Marques de1946-312072MiAaPQMiAaPQMiAaPQBOOK9910437918803321Minimum error entropy classification4192824UNINA