In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hy pertext classification, the labels of linked pages are highly correlated. A standard approach is to clas sify each entity independently, ignoring the correla tions between them. Recently, Probabilistic Relational Models, a relational version of Bayesian networks, were used to define a joint probabilistic model for a collection of related entities. In this paper, we present an alternative framework that builds on (conditional) Markov networks and addresses two limitations of the previous approach. First, undirected models do not im pose the acyclicity constraint that hinders representa tion of many important relational dependencies in di rected models. Second, undirected models are well suited for discriminative training, where we optimize the conditional likelihood of the labels given the fea tures, which generally improves classification accu racy. We show how to train these models effectively, and how to use approximate probabilistic inference over the learned model for collective classification of multiple related entities. We provide experimental re sults on a webpage classification task, showing that accuracy can be significantly improved by modeling relational dependencies.
However, their comparisons do not consider In REF , Tasker et al. present an alternative classification method where they build on Markov networks.
Discriminative Probabilistic Models for Relational Data
UAI 2002
arXiv: Learning
Computer Science Mathematics