Hybrid Random Fields

A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models

  • Antonino Freno
  • Edmondo Trentin

Part of the Intelligent Systems Reference Library book series (ISRL, volume 15)

Table of contents

  1. Front Matter
  2. Antonino Freno, Edmondo Trentin
    Pages 1-14
  3. Antonino Freno, Edmondo Trentin
    Pages 15-41
  4. Antonino Freno, Edmondo Trentin
    Pages 43-68
  5. Antonino Freno, Edmondo Trentin
    Pages 69-86
  6. Antonino Freno, Edmondo Trentin
    Pages 87-119
  7. Antonino Freno, Edmondo Trentin
    Pages 121-150
  8. Antonino Freno, Edmondo Trentin
    Pages 163-167
  9. Back Matter

About this book

Introduction

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 only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.

Keywords

Bayesian Networks Data Mining Density Estimation Hybrid Random Fields Intelligent Systems Kernel Methods Machine Learning Markov Random Fields Probabilistic Graphical Models

Authors and affiliations

  • Antonino Freno
    • 1
  • Edmondo Trentin
    • 1
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità degli Studi di Siena SienaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-20308-4
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-20307-7
  • Online ISBN 978-3-642-20308-4
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • About this book