Introduction statistical relational learning (20 résultats)

Hardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Hardcover. Etat : Fine. Etat de la jaquette : Fine. Fine, slightly oversized, soft smooth dust jacket, black cloth boards, bright clean pages, crisp tight copy.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New. In English.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Paperback. Etat : Brand New. 608 pages. 10.00x8.00x1.00 inches. In Stock.

Hardcover. Etat : New. In shrink wrap. Looks like an interesting title.

Hardcover. Etat : Very Good. Very Good .Ships From Multiple Locations. book.

Etat : New. Print on Demand.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Etat : New. Print on Demand.

Etat : New. PRINT ON DEMAND.

Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Taschenbuch. Etat : Neu. Introduction to Statistical Relational Learning | Lise Getoor (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | MIT Press | EAN 9780262538688 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.…

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.