Titlebook: Hybrid Random Fields; A Scalable Approach Antonino Freno,Edmondo Trentin Book 2011 Springer Berlin Heidelberg 2011 Bayesian Networks.Data

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https://doi.org/10.1007/978-3-642-20308-4Bayesian Networks; Data Mining; Density Estimation; Hybrid Random Fields; Intelligent Systems; Kernel Met

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Antonino Freno,Edmondo TrentinCovers the concepts and techniques related to the hybrid random field model for the first time.Offers a self-contained introduction to semiparametric and nonparametric density estimation.Written by le

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Intelligent Systems Reference Libraryhttp://image.papertrans.cn/h/image/430161.jpg

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978-3-642-26818-2Springer Berlin Heidelberg 2011

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Introduction,Joe Whittaker, 1990 [315]). Moreover, “[c]omplex computations, required to perform inference and learning in sophisticated models, can be expressed in terms of graphical manipulations, in which underlying mathematical expressions are carried along implicitly” (Christopher Bishop, 2006 [30]).

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1868-4394 arametric and nonparametric density estimation.Written by le.This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesti