| 书目名称 | Hybrid Random Fields |
| 副标题 | A Scalable Approach |
| 编辑 | Antonino Freno,Edmondo Trentin |
| 视频video | |
| 概述 | Covers 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 |
| 丛书名称 | Intelligent Systems Reference Library |
| 图书封面 |  |
| 描述 | .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 interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives..-- Manfred Jaeger, Aalborg Universitet.The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it..-- Marco Gori, Università degli Studi di Siena.Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they o |
| 出版日期 | Book 2011 |
| 关键词 | Bayesian Networks; Data Mining; Density Estimation; Hybrid Random Fields; Intelligent Systems; Kernel Met |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-20308-4 |
| isbn_softcover | 978-3-642-26818-2 |
| isbn_ebook | 978-3-642-20308-4Series ISSN 1868-4394 Series E-ISSN 1868-4408 |
| issn_series | 1868-4394 |
| copyright | Springer Berlin Heidelberg 2011 |
发表于 2025-3-21 17:43:07
