Learning Probabilistic Models of Relational Structure
L. Getoor, N. Friedman, D. Koller, and B. Taskar. Proc. 18th International Conf. on Machine Learning, page 170--177. Morgan Kaufmann, San Francisco, CA, (2001)
Abstract
Most real-world data is stored in relational form. In
contrast, most statistical learning methods work with
"flat" data representations, forcing us to convert our
data into a form that loses much of the relational struc-
ture. The recently introduced framework of probabilistic
relational models (PRMs) allows us to represent
probabilistic models over multiple entities that
utilize the relations between them. In this paper, we
propose the use of probabilistic models not only for
the...
%0 Conference Paper
%1 citeulike:365448
%A Getoor, Lise
%A Friedman, Nir
%A Koller, Daphne
%A Taskar, Benjamin
%B Proc. 18th International Conf. on Machine Learning
%D 2001
%I Morgan Kaufmann, San Francisco, CA
%K relationalmodels socialnets
%P 170--177
%T Learning Probabilistic Models of Relational Structure
%U http://citeseer.ist.psu.edu/517389.html
%X Most real-world data is stored in relational form. In
contrast, most statistical learning methods work with
"flat" data representations, forcing us to convert our
data into a form that loses much of the relational struc-
ture. The recently introduced framework of probabilistic
relational models (PRMs) allows us to represent
probabilistic models over multiple entities that
utilize the relations between them. In this paper, we
propose the use of probabilistic models not only for
the...
@inproceedings{citeulike:365448,
abstract = {Most real-world data is stored in relational form. In
contrast, most statistical learning methods work with
{"}flat{"} data representations, forcing us to convert our
data into a form that loses much of the relational struc-
ture. The recently introduced framework of probabilistic
relational models (PRMs) allows us to represent
probabilistic models over multiple entities that
utilize the relations between them. In this paper, we
propose the use of probabilistic models not only for
the...},
added-at = {2006-06-16T10:34:37.000+0200},
author = {Getoor, Lise and Friedman, Nir and Koller, Daphne and Taskar, Benjamin},
biburl = {https://www.bibsonomy.org/bibtex/259b86a4f9e5a7215794147c20e25e053/ldietz},
booktitle = {Proc. 18th International Conf. on Machine Learning},
citeulike-article-id = {365448},
comment = {easier / predecessor version of http://www.citeulike.org/user/ldietz/article/347487},
interhash = {0b81f943364bb7639807d026fb849a1a},
intrahash = {59b86a4f9e5a7215794147c20e25e053},
keywords = {relationalmodels socialnets},
pages = {170--177},
priority = {3},
publisher = {Morgan Kaufmann, San Francisco, CA},
timestamp = {2006-06-16T10:34:37.000+0200},
title = {Learning Probabilistic Models of Relational Structure},
url = {http://citeseer.ist.psu.edu/517389.html},
year = 2001
}