JMLR: Bayesian Network Learning with Parameter Constraints
- From: Redistributed <dnk@xxxxxxxxxxxxxxxxxxxxxxxx>
- Date: Sun, 09 Jul 2006 05:14:25 GMT
[[Redistributed from JMLR announce]]
~From: elm@xxxxxxxxxxxx
~Date: Fri, 7 Jul 2006 17:41:03 -0400
~Subject: [Jmlr-announce] Bayesian Network Learning with Parameter
Constraints
The Journal of Machine Learning Research (www.jmlr.org) is pleased to
announce publication of a new paper. The paper is part of the special
topic on Machine Learning and Large Scale Optimization:
------------------------------------------------------------------------------
Bayesian Network Learning with Parameter Constraints
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao
JMLR 7(Jul):1357--1383, 2006.
Abstract
The task of learning models for many real-world problems requires
incorporating domain knowledge into learning algorithms, to enable
accurate learning from a realistic volume of training data. This
paper considers a variety of types of domain knowledge for
constraining parameter estimates when learning Bayesian networks. In
particular, we consider domain knowledge that constrains the values
or relationships among subsets of parameters in a Bayesian network
with known structure.
We incorporate a wide variety of parameter constraints into learning
procedures for Bayesian networks, by formulating this task as a
constrained optimization problem. The assumptions made in module
networks, dynamic Bayes nets and context specific independence models
can be viewed as particular cases of such parameter constraints. We
present closed form solutions or fast iterative algorithms for
estimating parameters subject to several specific classes of
parameter constraints, including equalities and inequalities among
parameters, constraints on individual parameters, and constraints on
sums and ratios of parameters, for discrete and continuous variables.
Our methods cover learning from both frequentist and Bayesian points
of view, from both complete and incomplete data.
We present formal guarantees for our estimators, as well as methods
for automatically learning useful parameter constraints from data. To
validate our approach, we apply it to the domain of fMRI brain image
analysis. Here we demonstrate the ability of our system to first
learn useful relationships among parameters, and then to use them to
constrain the training of the Bayesian network, resulting in improved
cross-validated accuracy of the learned model. Experiments on
synthetic data are also presented.
------------------------------------------------------------------------------
This paper and previous papers are available electronically at http://
www.jmlr.org in PDF format. The papers of Volumes 1-4 were also
published in hardcopy by MIT Press; please see http://
mitpress.mit.edu/JMLR for details. Volume 5 and subsequent volumes
are being printed in hardcopy by Microtome Publishing. Please see
http://www.mtome.com/Publications/JMLR/jmlr.html for details and
ordering information.
-Erik G. Learned-Miller
elm@xxxxxxxxxxxx
[ comp.ai is moderated ... your article may take a while to appear. ]
.
- Prev by Date: JMLR: Linear Programs for Hypotheses Selection in Probabilistic Inference Models
- Next by Date: CFP: Workshop on Reliability Issues in Knowledge Disccovery (RIKD06)
- Previous by thread: JMLR: Linear Programs for Hypotheses Selection in Probabilistic Inference Models
- Next by thread: CFP: Workshop on Reliability Issues in Knowledge Disccovery (RIKD06)
- Index(es):
Relevant Pages
|
|
