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Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

by

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) Cover

 

Synopses & Reviews

Publisher Comments:

andlt;Pandgt;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.Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.andlt;/Pandgt;

Synopsis:

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.

Synopsis:

andlt;Pandgt;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.andlt;/Pandgt;

Synopsis:

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

Synopsis:

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.Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.

About the Author

Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland.Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.

Product Details

ISBN:
9780262072885
Author:
Getoor, Lise
Publisher:
MIT Press (MA)
Editor:
Taskar, Benjamin
Editor:
Taskar, Ben
Author:
Ramakrishnan, Raghu
Author:
Marthi, Bhaskara
Author:
Popescul, Alexandrin
Author:
eroski
Author:
Muggleton, Stephen
Author:
Shavlik, Jude
Author:
Mooney, Raymond J.
Author:
Taskar, Benjamin
Author:
Page, David
Author:
Jensen, David
Author:
Pfeffer, Avi
Author:
Raedt, Luc De
Author:
Milch, Brian
Author:
Burnside, Elizabeth
Author:
Amir, Eyal
Author:
Neville, Jennifer
Author:
Dutra, Ines
Author:
Pahlavi, Niels
Author:
Heckerman, David
Author:
Fern, Alan
Author:
Sutton, Charles
Author:
Davis, Jesse
Author:
Taskar, Ben
Author:
Domingos, Pedro
Author:
Ong, Daniel L.
Author:
Do
Author:
Bunescu, Razvan
Author:
Ungar, Lyle H.
Author:
De Raedt, Luc
Author:
Kolobov, Andrey
Author:
ž
Author:
Friedman, Nir
Author:
Braz, Roderigo de Salvo
Author:
Massachusetts Institute of Technology
Author:
Costa, Vitor Santos
Author:
eroski, Sa
Author:
Koller, Daphne
Author:
Roth, Dan
Author:
Russell, Stuart
Author:
Cussens, James
Author:
Givan, Robert
Author:
o
Author:
Wong, Ming-Fai
Author:
Meek, Chris
Author:
š
Author:
Yoon, SungWook
Author:
S., A.
Author:
Kersting, Kristian
Author:
Richardson, Matthew
Author:
McCallum, Andrew
Author:
Yih, Wen-tau
Author:
Abbeel, Pieter
Author:
D
Author:
Sontag, David
Location:
Cambridge
Subject:
Logic Design
Subject:
Machine Theory
Subject:
Relational databases
Subject:
Computer algorithms
Subject:
Computers-Reference - General
Copyright:
Series:
Adaptive Computation and Machine Learning series Introduction to Statistical Relational Learning
Publication Date:
20070831
Binding:
HARDCOVER
Grade Level:
from 17
Language:
English
Illustrations:
134 fig, 42 tbl illus.
Pages:
608
Dimensions:
10 x 8 in

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Related Subjects

Computers and Internet » Artificial Intelligence » Fuzzy Logic
Computers and Internet » Computers Reference » General
Education » Teaching » Math and Science

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) New Hardcover
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Product details 608 pages Mit Press - English 9780262072885 Reviews:
"Synopsis" by , 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.
"Synopsis" by , andlt;Pandgt;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.andlt;/Pandgt;
"Synopsis" by , 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
"Synopsis" by , 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.Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.
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