Connectionists: Book Announcement: Introduction to Statistical Relational Learning

[Lise Getoor]

Introduction to Statistical Relational Learning
Edited by Lise Getoor and Ben Taskar
ISBN-10: 0-262-07288-2
Published by The MIT Press, 601 pages
http://www.cs.umd.edu/srl-book/

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.