Keskeiset käsitteet
MLNs' generalization behavior across domain sizes is influenced by parameter variance, with methods reducing variance improving generalization.
Tiivistelmä
The content discusses the study on understanding domain-size generalization in Markov Logic Networks (MLNs). It delves into the inconsistency of parameter estimation in relational data and formalizes this inconsistency. The paper provides theoretical results that justify reducing parameter variance to improve generalization. Empirical verification is done through experiments on various datasets using different approaches like L1 regularization, L2 regularization, and Domain-Size Aware MLNs. Results show that reducing parameter variance consistently enhances dataset likelihoods over larger domains.
Structure:
Introduction to MLNs and Generalization Behavior
Relational data's inconsistency in parameter estimation.
Formalizing the inconsistency and justifying variance reduction.
Experiments and Methodology
Evaluation of approaches on different datasets.
Comparison of methods for reducing parameter variance.
Results Analysis
Improvement in target set likelihood with reduced variance methods.
Conclusion and Acknowledgments
Tilastot
"We empirically verify our results on four different datasets."
"For each of the four datasets, methods that reduce parameter variance consistently improve target set likelihood by several orders of magnitude."