Introduction to Statistical Relational Learning

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Lise Getoor, Ben Taskar
MIT Press, 2007 - Computers - 586 pages
2 Reviews

Handling inherent uncertainty and exploiting compositional structure are fundamentalto understanding and designing large-scale systems. Statistical relational learning builds on ideasfrom probability theory and statistics to address uncertainty while incorporating tools from logic,databases and programming languages to represent structure. In Introduction to StatisticalRelational Learning, leading researchers in this emerging area of machine learning describe currentformalisms, models, and algorithms that enable effective and robust reasoning about richlystructured systems and data. The early chapters provide tutorials for material used in laterchapters, offering introductions to representation, inference and learning in graphical models, andlogic. The book then describes object-oriented approaches, including probabilistic relationalmodels, relational Markov networks, and probabilistic entity-relationship models as well aslogic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logicprograms. Later chapters discuss such topics as probabilistic models with unknown objects,relational dependency networks, reinforcement learning in relational domains, and informationextraction. By presenting a variety of approaches, the book highlights commonalities and clarifiesimportant differences among proposed approaches and, along the way, identifies importantrepresentational and algorithmic issues. Numerous applications are provided throughout.Lise Getooris Assistant Professor in the Department of Computer Science at the University of Maryland. BenTaskar is Assistant Professor in the Computer and Information Science Department at the Universityof Pennsylvania.

  

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Review: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)

User Review  - Lurino - Goodreads

for those who are interested in machine learning. very technical introductions, but not totally unapproachable. the content is more like a collection of papers about certain topics, and very ... Read full review

Review: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)

User Review  - 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. Read full review

Contents

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
Copyright

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About the author (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.

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