文档介绍：computer science/statistics INTRODUCTION TO
INTRODUCTION TO
Lise Getoor is Assistant Professor in the Of related interest STATISTICAL RELATIONAL LEARNING STATISTICAL RELATIONAL
Department puter Science at the LEARNING
University of Maryland. Ben Taskar is Assistant GAUSSIAN PROCESSES FOR MACHINE LEARNING
Professor in puter and Information Carl Edward Rasmussen and Christopher K. I. Williams GETOOR AND TASKAR, STATISTICAL RELATIONAL LEARNING INTRODUCTION TO
EDITED BY
Science Department at the University of EDITED BY LISE GETOOR AND BEN TASKAR
LISE GETOOR AND BEN TASKAR
Pennsylvania. Gaussian processes (GPs) provide a principled, practical, probabilistic approach
to learning in kernel machines. GPs have received increased attention in the Handling inherent uncertainty and exploiting
machine-munity over the past decade, and this book positional structure are fundamental to under-
putation and Machine Learning series a long-needed systematic and unified treatment of theoretical and prac- standing and designing large-scale systems.
tical aspects of GPs in machine learning. The treatment pre- Statistical relational learning builds on ideas from
hensive and self-contained, targeted at researchers and students in probability theory and statistics to address uncer-
machine learning and applied statistics. tainty while incorporating tools from logic, data-
bases, and programming languages to represent
structure. In Introduction to Statistical Relational
EDITORS Learning, leading researchers in this emerging
area of machine learning describe current for-
SEMI-SUPERVISED LEARNING
E malisms, models, and algorithms that enable
edited by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien
effective and robust reasoning about richly struc-
tured systems and data.
In the field of machine learning, semi-supervised learning (SSL) occupies
The early chapters provide tutorials for mate-
the middle ground between supervised le