Z. Kou and W. Cohen. SDM 2007

Citation

Zhenzhen Kou and William W. Cohen. Stacked Graphical Models for Efficient Inference in Markov Random Fields in SDM-2007.

Online version

Stacked Graphical Models

Summary

This paper is an extension of Stacked Sequential Learning and shows how stacking can be used in non-sequential tasks, such as text region detection and document classification.

The key points of the paper are:

• arbitrary base learner
• very fast inference time, 40 to 80 times faster than Gibbs sampling.
• relational information is captured by augmenting data with the predictions of related instances.
• Authors showed its applications on the problems where the relation of instances is not sequential.

Example Stacked Graphical Models Usage

text region detection

• Task is to find the text regions in figures, where each figure may contain several panels.
• Relational information comes from: the prediction of other candidates within the same panel and the predictions of four neighbor panels (up, down, right and left).
• The predictions of the candidate region and neighbor regions return a vector of binary features indicating whether a character is found in those regions.

webpage classification

• Task is to classify webpages on the WebKB dataset.
• Relational information comes from: the number of incoming and outgoing links in each category.

NER

• Relational information comes from nearby words. In this case Stacked Graphical Model is the same with SSL.