Hybrid Random Fields : A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
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 add...
Shranjeno v:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Izdaja: | 1st ed. 2011. |
| Serija: | Intelligent Systems Reference Library
15 |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Hybrid Random Fields, Texte imprimé, 9783642203077 • Hybrid Random Fields, Texte imprimé, 9783642268182 • Hybrid Random Fields, Texte imprimé, 9783642203077 • Hybrid Random Fields, Texte imprimé, 9783642203091 • Hybrid Random Fields, Texte imprimé, 9783642268182 • Hybrid Random Fields, Texte imprimé, 9783642203077 • Hybrid Random Fields, Texte imprimé, 9783642203091 |
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| 100 | 1 | |a Freno, Antonino. | |
| 245 | 1 | 0 | |a Hybrid Random Fields : |b A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models |c by Antonino Freno, Edmondo Trentin. |
| 250 | |a 1st ed. 2011. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Intelligent Systems Reference Library |v 15 |x 1868-4408 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Introduction Bayesian Networks Markov Random Fields Introducing Hybrid Random Fields: Discrete-Valued Variables Extending Hybrid Random Fields: Continuous-Valued Variables Applications Probabilistic Graphical Models: Cognitive Science or Cognitive Technology? . Conclusions | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 | ||
| 700 | 1 | |a Trentin, Edmondo. |4 aut | |
| 776 | 0 | |t Hybrid Random Fields |b Texte imprimé |z 9783642203077 | |
| 776 | 0 | |t Hybrid Random Fields |b Texte imprimé |z 9783642268182 | |
| 776 | 0 | |t Hybrid Random Fields |b Texte imprimé |z 9783642203077 | |
| 776 | 0 | |t Hybrid Random Fields |b Texte imprimé |z 9783642203091 | |
| 776 | 0 | |t Hybrid Random Fields |b Texte imprimé |z 9783642268182 | |
| 776 | 0 | |t Hybrid Random Fields |b Texte imprimé |z 9783642203077 | |
| 776 | 0 | |t Hybrid Random Fields |b Texte imprimé |z 9783642203091 | |
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