9783642268182 - hybrid random fields: a scalable approach to structure and parameter learning in probabilistic graphical models: 15 (intelligent systems reference library) de freno, antonino; trentin, edmondo (12 resultados)

Idioma: Inglés
Editorial: Springer, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Editorial: Springer, 2013
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Condición: New. pp. 228.

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Editorial: Springer-Verlag New York Inc, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Paperback. Condición: Brand New. 2011 edition. 228 pages. 9.13x6.06x0.55 inches. In Stock.

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Editorial: Springer Berlin Heidelberg, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 approa…ch 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 UniversitetThe 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 SienaGraphical 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.

Idioma: Inglés
Editorial: Springer, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Editorial: Springer, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Editorial: Springer, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condición: new. Questo è un articolo print on demand.

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Editorial: Springer Berlin Heidelberg Jul 2013, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 in…teresting 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 UniversitetThe 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 SienaGraphical 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. 228 pp. Englisch.

Idioma: Inglés
Editorial: Springer Berlin Heidelberg, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Librería: moluna, Greven, Alemaniamoluna
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 leading experts in…the fieldThi.

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Editorial: Springer, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Condición: New. Print on Demand pp. 228 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Idioma: Inglés
Editorial: Springer, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Condición: New. PRINT ON DEMAND pp. 228.

Idioma: Inglés
Editorial: Springer, Springer Vieweg Jul 2013, 2013
Serie: Intelligent Systems Reference Library, Libro 9 de 188. Libro 9 de 188 - Intelligent Systems Reference Library
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 intere…sting 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 228 pp. Englisch.