00947nam0 22002773i 450 PUV021913820231121125610.0019823964520120911d1991 ||||0itac50 baenggbz01i xxxe z01nIn the interest of the governeda study in Bentham's philosophy of utility and lawby David LyonsRevised edOxfordClarendon Press1991XXII, 153 p.23 cm.Lyons, DavidMILV011173070304303ITIT-0120120911IT-FR0017 Biblioteca umanistica Giorgio ApreaFR0017 PUV0219138Biblioteca umanistica Giorgio Aprea 52MAG 14/1239 52FSS0000011105 VMN RS A 2012091120120911 52In the interest of the governed3613280UNICAS02642nam 2200505Ia 450 991043805710332120200520144314.03-642-38652-010.1007/978-3-642-38652-7(OCoLC)847735622(MiFhGG)GVRL6WIW(CKB)2670000000371295(MiAaPQ)EBC1317208(EXLCZ)99267000000037129520130314d2013 uy 0engurun|---uuuuatxtccrDimensionality reduction with unsupervised nearest neighbors /Oliver Kramer1st ed. 2013.Dordrecht Springer20131 online resource (xviii, 130 pages) illustrations (some color)Intelligent systems reference library ;51"ISSN: 1868-4394."3-642-38651-2 Includes bibliographical references and index.Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions.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 illustrate the introduced concepts and to highlight the experimental results.  .Intelligent systems reference library ;v. 51.DimensionsData miningDimensions.Data mining.006.31519.5/36Kramer Oliver761919MiAaPQMiAaPQMiAaPQBOOK9910438057103321Dimensionality Reduction with Unsupervised Nearest Neighbors2513616UNINA