Efficient first-order probabilistic models for inference and learning
A probabilistic model is any formalism to specify a complex probability
distribution. Such formalisms facilitate uncertainty handling and
evidential reasoning in artificial intelligence. Current probabilistic
models restrict their variables to simple boolean propositions, discrete
attributes, or numbers. The goal of this project was to enhance these models
with the power of first-order logic. This enables the variables to range
over complex structured objects, be they molecules or websites.
has proposed several new methods for specifying such models, reasoning with them, and
learning them from data. The approach uses the individual-centred
representations that are a central topic of study in recent work in machine
learning and inductive logic programming. Possible domains of application
include molecular biology, drug design, information retrieval on the web,
and user modelling. Experimental validation of the utility of first-order
probabilistic models has been carried on several of these domains.
Staff and Students
and Nicolas Lachiche.
Probabilistic reasoning with terms.
To appear in
Electronic Transactions in Artificial Intelligence.
[Submitted version, PDF]
and N. Lachiche.
Naive Bayesian classification of structured data.
Machine Learning 57(3): 233--269, 2004.
N. Lachiche and P. Flach.
1BC2: a true
classifier. In: Proceedings of the 12th
International Conference on Inductive
Logic Programming, pages 133--148. Springer-Verlag,
Gyftodimos and Peter
Bayesian Networks: an Approach to Classification and
Learning for Structured Data. In: Proceedings of
the ECML/PKDD - 2003 Workshop on Probablistic Graphical
Models for Classification, pages 25--36. Ruder
Boskovic Institute, Zagreb, Croatia, September 2003.
Elias Gyftodimos and Peter Flach. Hierarchical
Bayesian Networks: A Probabilistic Reasoning Model for
Structured Domains. In: Proceedings of the
ICML-2002 Workshop on Development of
Representations, Edwin de Jong and Tim Oates,
editors, pages 23--30. The University of New South
Wales, July 2002.
This research was supported by EPSRC research grant GR/N07394.