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Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models

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Feldname	Details
Vorliegende Sprache	eng
Hinweise auf parallele Ausgaben	347008208 Buchausg. u.d.T.: ‡Freno, Antonino: Hybrid random fields
ISBN	978-3-642-20307-7
Name	Freno, Antonino
Name	Trentin, Edmondo
Name ANZEIGE DER KETTE	Trentin, Edmondo
T I T E L	Hybrid Random Fields
Zusatz zum Titel	A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
Verlagsort	Berlin, Heidelberg
Verlag	Springer Berlin Heidelberg
Erscheinungsjahr	2011
Erscheinungsjahr	2011
Umfang	Online-Ressource (XVIII, 210p. 17 illus, digital)
Reihe	Intelligent Systems Reference Library ; 15
Notiz / Fußnoten	Includes bibliographical references and index
Weiterer Inhalt	Title; Contents; Introduction; Manifesto; Statistics, Graphs, and Beyond; Probabilistic Graphical Models; A Piece of History; Overview of the Book; Bayesian Networks; Introduction; Representation of Probabilities; Parameter Learning; Structure Learning; The Naive Bayes Classifier; Final Remarks; Markov Random Fields; Introduction; Representation of Probabilities; Parameter Learning; Structure Learning; Final Remarks; Introducing Hybrid Random Fields: Discrete-Valued Variables; Introduction; Representation of Probabilities; Formal Properties; Inference; Parameter Learning; Structure Learning. Related WorkFinal Remarks; Extending Hybrid Random Fields: Continuous-Valued Variables; Introduction; Conditional Density Estimation; Parametric Hybrid Random Fields; Semiparametric Hybrid Random Fields; Nonparametric Hybrid Random Fields; Structure Learning; Final Remarks; Applications; Introduction; Selecting Features by Learning Markov Blankets; Application to Discrete Domains; Pattern Classification in Continuous Domains; Final Remarks; Probabilistic Graphical Models: Cognitive Science or Cognitive Technology?; Introduction; A Philosophical View of Artificial Intelligence. From Cognitive Science to Cognitive TechnologyStatistical Machine Learning and the Philosophy of Science; Final Remarks; Conclusions; Hybrid Random Fields: Where Are We Now?; Future Research: Where Do We Go from Here?; Probability Theory; Graph Theory; References; Index;
Titelhinweis	Buchausg. u.d.T.: ‡Freno, Antonino: Hybrid random fields
ISBN	ISBN 978-3-642-20308-4
Klassifikation	UYQ
	COM004000
	*60-02
	60G60
	62M02
	62F15
	68R10
	68T05
	05C90
	006.3
	001.434072
	Q342
Kurzbeschreibung	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.
1. Schlagwortkette	Graphisches Modell
	Bayes-Netz
	Markov-Zufallsfeld
SWB-Titel-Idn	343196808
Signatur	Springer E-Book
Bemerkungen	Elektronischer Volltext - Campuslizenz
Elektronische Adresse	$uhttp://dx.doi.org/10.1007/978-3-642-20308-4
Internetseite / Link	Volltext
Siehe auch	Volltext
Siehe auch	Cover
Siehe auch	Inhaltstext

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