2002 DiscriminativeProbabilisticModelsForRelData

Subject Headings: Global Collective Classification Algorithm.

Notes

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 hypertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, ignoring the correlations 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 impose the acyclicity constraint that hinders representation of many important relational dependencies in directed models. Second, undirected models are well suited for discriminative training, where we optimize the conditional likelihood of the labels given the features, which generally improves classification accuracy. 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 results on a webpage classification task, showing that accuracy can be significantly improved by modeling relational dependencies.

We propose the use of a joint probabilistic model for an entire collection of related entities. Following the approach of Lafferty (2001), we base our approach on discriminatively trained undirected graphical models, or Markov networks (Pearl 1988). We introduce the framework of relational Markov network (RMNs), which compactly defines a Markov network over a relational data set. The graphical structure of an RMN is based on the relational structure of the domain, and can easily model complex patterns over related entities.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 DiscriminativeProbabilisticModelsForRelData	Daphne Koller Pieter Abbeel			Discriminative Probabilistic Models for Relational Data			http://ai.stanford.edu/~koller/Papers/Taskar+al:UAI02.pdf