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.
Author(s): Lise Getoor, Ben Taskar
Series: Adaptive computation and machine learning
Publisher: MIT Press
Year: 2007
Language: English
Pages: 602
City: Cambridge, Mass
Contents......Page 6
Series Foreword......Page 12
Preface......Page 14
1 - Introduction......Page 16
2 - Graphical Models in a Nutshell......Page 28
3 - Inductive Logic Programming in a Nutshell......Page 72
4 - An Introduction to Conditional Random Fields for Relational Learning......Page 108
5 - Probabilistic Relational Models......Page 144
6 - Relational Markov Networks......Page 190
7 - Probabilistic Entity-Relationship Models, PRMs, and Plate Models......Page 216
8 - Relational Dependency Networks......Page 254
9 - Logic-based Formalisms for Statistical Relational Learning......Page 284
10 - Bayesian Logic Programming: Theory and Tool......Page 306
11 - Stochastic Logic Programs: A Tutorial......Page 338
12 - Markov Logic: A Unifying Framework for Stastical Relational Learning......Page 354
13 - BLOG: Probabilistic Models with Unknown Objects......Page 388
14 - The Design and Implementation of IBAL: A General-Purpose Probabilistic Language......Page 414
15 - Lifted First-Order Probabilistic Inference......Page 448
16 - Feature Generation and Selection in Multi-Relational Statistical Learning......Page 468
17 - Learning a New View of a Database: With an Application in Mammography......Page 492
18 - Reinforcement Learning in Relational Domains: A Policy-Language Approach......Page 514
19 - Statistical Relational Learning for Natural Language Information Extraction......Page 550
20 - Global Inference for Entity and Relation Identification via a Linear Programming Formulation......Page 568
Contributors......Page 596
Index......Page 602
