Learning models of relational stochastic processes

Authors:
Sumit Sanghai;Pedro Domingos;Daniel Weld
Affiliations:
University of Washington, Seattle, WA;University of Washington, Seattle, WA;University of Washington, Seattle, WA
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

Citing 10
Cited 3

Clausal Discovery

Machine Learning - special issue on inductive logic programming
Inducing Features of Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence
Structured representation of complex stochastic systems

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Bursty and hierarchical structure in streams

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Markov models and their application to adaptive web navigation

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic bayesian networks: representation, inference and learning

Dynamic bayesian networks: representation, inference and learning
Overview of the 2003 KDD Cup

ACM SIGKDD Explorations Newsletter
Learning the structure of Markov logic networks

ICML '05 Proceedings of the 22nd international conference on Machine learning
Markov logic networks

Machine Learning
Dynamic probabilistic relational models

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

An Inductive Logic Programming Approach to Statistical Relational Learning

Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Learning models of macrobehavior in complex adaptive systems

AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Recognizing activities with multiple cues

Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Processes involving change over time, uncertainty, and rich relational structure are common in the real world, but no general algorithms exist for learning models of them. In this paper we show how Markov logic networks (MLNs), a recently developed approach to combining logic and probability, can be applied to time-changing domains. We then show how existing algorithms for parameter and structure learning in MLNs can be extended to this setting. We apply this approach in two domains: modeling the spread of research topics in scientific communities, and modeling faults in factory assembly processes. Our experiments show that it greatly outperforms purely logical (ILP) and purely probabilistic (DBN) learners.