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

Getoor, Lise [Editor]; Taskar, Ben [Editor]; Koller, Daphne [Contributor]; Friedman, Nir [Contributor]; Getoor, Lise [Contributor]; Taskar, Ben [Contributor]; D?eroski, Sa?o [Contributor]; Sutton, Charles [Contributor]; McCallum, Andrew [Contributor]; Pfeffer, Avi [Contributor]; Abbeel, Pieter [Contributor]; Wong, Ming-Fai [Contributor]; Heckerman, David [Contributor]; Meek, Chris [Contributor]; Neville, Jennifer [Contributor]; Jensen, David [Contributor]; Cussens, James [Contributor]; Kersting, Kristian [Contributor]; Raedt, Luc de [Contributor]; Muggleton, Stephen [Contributor]; Pahlavi, Niels [Contributor]; Domingos, Pedro [Contributor]; Richardson, Matthew [Contributor]; Milch, Brian [Contributor]; Marthi, Bhaskara [Contributor]; Russel

ISBN 10: 0262072882 / ISBN 13: 9780262072885

Published by The MIT Press, 2007

About this Item

In shrink wrap. Looks like an interesting title!

Seller Inventory # Q-0262072882

Bibliographic Details

• Title: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning series)
• Publisher: The MIT Press
• Publication Date: 2007
• Language: English
• ISBN 10: 0262072882
• ISBN 13: 9780262072885
• Binding: Hardcover
• Condition: New
• Weight: 45 ounces

About this title

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.

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.

About the Authors

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.

Daphne Koller is Professor in the Department of Computer Science at Stanford University.

Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University.

Pieter Abbeel is Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley.