Introduction to Statistical Relational Learning
Edited by Lise Getoor and Ben Taskar
Published by The MIT Press

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.

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Table of Contents
  1. Introduction
    Lise Getoor, Ben Taskar
  2. Graphical Models in a Nutshell
    Daphne Koller, Nir Friedman, Lise Getoor, Ben Taskar
  3. Inductive Logic Programming in a Nutshell
    Saso Dzeroski
  4. An Introduction to Conditional Random Fields for Relational Learning
    Charles Sutton, Andrew McCallum
  5. Probabilistic Relational Models
    Lise Getoor, Nir Friedman, Daphne Koller, Avi Pfeffer, Ben Taskar
  6. Relational Markov Networks
    Ben Taskar, Pieter Abbeel, Ming-Fai Wong, Daphne Koller
  7. Probabilistic Entity-Relationship Models, PRMs, and Plate Models
    David Heckerman, Chris Meek, Daphne Koller
  8. Relational Dependency Networks
    Jennifer Neville, David Jensen
  9. Logic-based Formalisms for Statistical Relational Learning
    James Cussens
  10. Bayesian Logic Programming: Theory and Tool
    Kristian Kersting, Luc De Raedt
  11. Stochastic Logic Programs: A Tutorial
    Stephen Muggleton, Niels Pahlavi
  12. Markov Logic: A Unifying Framework for Statistical Relational Learning
    Pedro Domingos, Matthew Richardson
  13. BLOG: Probabilistic Models with Unknown Objects
    Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, Andrey Kolobov
  14. The Design and Implementation of IBAL: A General-Purpose Probabilistic Language
    Avi Pfeffer
  15. Lifted First-Order Probabilistic Inference
    Rodrigo de Salvo Braz, Eyal Amir, Dan Roth
  16. Feature Generation and Selection in Multi-Relational Statistical Learning
    Alexandrin Popescul, Lyle H. Ungar
  17. Learning a New View of a Database: With an Application in Mammography
    Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Raghu Ramakrishnan, Jude Shavlik, Vitor Santos Costa
  18. Reinforcement Learning in Relational Domains: A Policy-Language Approach
    Alan Fern, SungWook Yoon, Robert Givan
  19. Statistical Relational Learning for Natural Language Information Extraction
    Razvan C. Bunescu, Raymond J. Mooney
  20. Global Inference for Entity and Relation Identification via a Linear Programming Formulation
    Dan Roth, Wen-tau Yih

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