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eBay item number:305375451337
Item specifics
- Condition
- PublishedOn
- 2011-05-26
- Title
- Hybrid Random Fields: A Scalable Approach to Structure and Param
- Artist
- Not Specified
- ISBN
- 9783642203077
- Publication Name
- Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
- Publisher
- Springer-Verlag Berlin AND Heidelberg Gmbh & Co. KG
- Subject
- Computer Science
- Publication Year
- 2011
- Series
- Intelligent Systems Reference Library
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 235 mm
- Item Weight
- 1100 g
- Item Width
- 155 mm
- Number of Pages
- 210 Pages
About this product
Product Information
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, Universita 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.
Product Identifiers
Publisher
Springer-Verlag Berlin AND Heidelberg Gmbh & Co. KG
ISBN-13
9783642203077
eBay Product ID (ePID)
176360458
Product Key Features
Number of Pages
210 Pages
Publication Name
Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
Language
English
Subject
Computer Science
Publication Year
2011
Type
Textbook
Series
Intelligent Systems Reference Library
Format
Hardcover
Dimensions
Item Height
235 mm
Item Weight
1100 g
Item Width
155 mm
Volume
15
Additional Product Features
Country/Region of Manufacture
Germany
Item description from the seller
Seller business information
VAT number: GB 724498118
CRN: 03800600
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