Introduction to Statistical Relational Learning.pdf
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EDITED BY LISE GETOOR AND BEN TASKAR
STATISTICAL RELATIONAL LEARNING
INTRODUCTION TO
INTRODUCTION TO
STATISTICAL RELATIONAL
LEARNING
EDITED BY
LISE GETOOR AND BEN TASKAR
Handling inherent uncertainty and exploiting
compositional structure are fundamental to under-
standing and designing large-scale systems.
Statistical relational learning builds on ideas from
probability theory and statistics to address uncer-
tainty while incorporating tools from logic, data-
bases, and programming languages to represent
structure. In
Introduction to Statistical Relational
Learning,
leading researchers in this emerging
area of machine learning describe current for-
malisms, models, and algorithms that enable
effective and robust reasoning about richly struc-
tured systems and data.
The early chapters provide tutorials for mate-
rial used in later chapters, offering introductions
to representation, inference and learning in graph-
ical 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 pro-
grams. Later chapters discuss such topics as
probabilistic models with unknown objects, rela-
tional dependency networks, reinforcement learn-
ing in relational domains, and information extraction.
By presenting a variety of approaches, the
book highlights commonalities and clarifies impor-
tant differences among proposed approaches and,
along the way, identifies important representational
and algorithmic issues. Numerous applications are
provided throughout.
INTRODUCTION TO
STATISTICAL RELATIONAL LEARNING
GETOOR AND TASKAR, EDITORS
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.
Adaptive Computation and Machine Learning series
computer science/statistics
Of related interest
GAUSSIAN PROCESSES FOR MACHINE LEARNING
Carl Edward Rasmussen and Christopher K. I. Williams
Gaussian processes (GPs) provide a principled, practical, probabilistic approach
to learning in kernel machines. GPs have received increased attention in the
machine-learning community over the past decade, and this book provides
a long-needed systematic and unified treatment of theoretical and prac-
tical aspects of GPs in machine learning. The treatment is compre-
hensive and self-contained, targeted at researchers and students in
machine learning and applied statistics.
The MIT Press
Massachusetts Institute of Technology
Cambridge, Massachusetts 02142
http://mitpress.mit.edu
SEMI-SUPERVISED LEARNING
edited by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien
In the field of machine learning, semi-supervised learning (SSL) occupies
the middle ground between supervised learning (in which all training exam-
ples are labeled) and unsupervised learning (in which no label data are
given). Interest in SSL has increased in recent years, particularly because of
application domains in which unlabeled data are plentiful, such as images,
text, and bioinformatics. This first comprehensive overview of SSL presents
state-of-the-art algorithms, a taxonomy of the field, selected applications,
benchmark experiments, and perspectives on ongoing and future research.
978-0-262-07288-5
0-262-07288-2
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Cover design based on an animation by MAXAM,
http://www.maxamania.com
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