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Introduction to statistical relational learning

Frontcover
Lise Getoor, Ben Taskar
2 Rezensionen
MIT Press, 2007 - 586 Seiten
"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."--Publisher's website.
  

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Review: Introduction to Statistical Relational Learning

Nutzerbericht  - Chimezie Ogbuji - Goodreads

Excellent source for everything on the topic of statistical relational learning (Recommended by my AI / Machine Learning professor). Covers foundational topics, current research, and future topics as well. Vollständige Rezension lesen

Review: Introduction to Statistical Relational Learning

Nutzerbericht  - Ondřej Sýkora - Goodreads

Obviously, this is a collection of papers on a diverse set topics, rather than a textbook on a single subject. Though, the papers are mostly written in a way that they are quite approachable without ... Vollständige Rezension lesen

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Inhalt

1 Introduction
1
2 Graphical Models in a Nutshell
13
3 Inductive Logic Programming in a Nutshell
57
4 An Introduction to Conditional Random Fields for Relational Learning
93
5 Probabilistic Relational Models
129
6 Relational Markov Networks
175
7 Probabilistic EntityRelationship Models PRMs and Plate Models
201
8 Relational Dependency Networks
239
Probabilistic Models with Unknown Objects
373
A GeneralPurpose Probabilistic Language
399
15 Lifted FirstOrder Probabilistic Inference
433
16 Feature Generation and Selection in MultiRelational Statistical Learning
453
With an Application in Mammography
477
A PolicyLanguage Approach
499
19 Statistical Relational Learning for Natural Language Information Extraction
535
20 Global Inference for Entity and Relation Identification via a Linear Programming Formulation
553

9 Logicbased Formalisms for Statistical Relational Learning
269
Theory and Tool
291
A Tutorial
323
A Unifying Framework for Stastical Relational Learning
339
Contributors
581
Index
587
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Referenzen von Webseiten

Introduction to Statistical Relational Learning
In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, ...
www.cs.umd.edu/ srl-book/

Introduction to Statistical Relational Learning - Blackwell Online
Introduction to Statistical Relational Learning, L Getoor, Computers Books - Blackwell Online Bookshop.
bookshop.blackwell.co.uk/ jsp/ id/ Introduction_to_Statistical_Relational_Learning/ 9780262072885

CS590N: Statistical Relational Learning
Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, editors, to appear. February 1: Invited Talk ...
www.cs.purdue.edu/ homes/ neville/ courses/ CS590N-schedule.html

An Introduction to Statistical Relational Learning
An Introduction to Statistical Relational Learning. author: Lise Getoor, University of Maryland. You might be experiencing some problems with Your Video ...
videolectures.net/ ecml07_getoor_isr/

citeulike: An Introduction to Conditional Random Fields for ...
@incollection{suttonmccallum2006, author = {Sutton, C. and Mccallum, A. }, booktitle = {Introduction to Statistical Relational Learning}, ...
www.citeulike.org/ group/ 4300/ article/ 2335583

uoy - CS - AIG - Statistical Relational Learning
In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning . pages 269-290, MIT Press, Cambridge, MA, 2007. James Cussens. ...
www.cs.york.ac.uk/ aig/ srl/

UT ML Group: Statistical Relational Learning
Introduction to Statistical Relational Learning , Getoor, L. and Taskar, B. (Eds.), pp. 535-552, MIT Press, Cambridge, MA, 2007. ...
www.cs.utexas.edu/ ~ml/ publication/ srl.html

april 2 - Book chapters
In L. Getoor and B. Taskar, editorr, An Introduction to Statistical Relational Learning, MIT Press, 2006. (To appear). sh Muggleton, H. Lodhi, A. Amini, ...
choyu.informatik.uni-freiburg.de/ APrIL2/ men4/ men4_6/ index_html?printable=1

Daphne Koller
"Probabilistic Entity-Relationship Models, prms, and Plate Models." In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning. ...
ai.stanford.edu/ ~koller/ papers.cgi?year=2005

Statistical relational learning with nonparametric Bayesian models
Statistical Relational Learning with. Nonparametric Bayesian Models. Dissertation im Fach Informatik. an der Fakultat fur Mathematik, Informatik und ...
edoc.ub.uni-muenchen.de/ 7619/ 1/ xu_zhao.pdf

Über den Autor (2007)

Lise Getoor is Assistant Professor in the Department of Computer Science at the University ofMaryland.

Ben Taskar is Assistant Professor in the Computer and Information Science Department at theUniversity of Pennsylvania.

Bibliografische Informationen