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

HPA533

FLEET

雇傭兵

閃光東本

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

共同時(shí)代

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

起皺紋

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

Biofeedback

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

競(jìng)選運(yùn)動(dòng)

混亂生活

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]).

motor-unit

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