Difference between revisions of "Z. Kou and W. Cohen. SDM 2007"
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== Summary == | == Summary == | ||
This paper is an extension of Stacked Sequential Learning and shows how stacking can be used in non-sequential tasks, such as | 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. | + | text region detection and document classification. |
The key points of the paper are: | The key points of the paper are: | ||
| Line 15: | Line 15: | ||
* very fast inference time, 40 to 80 times faster than Gibbs sampling. | * 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. | * relational information is captured by augmenting data with the predictions of related instances. | ||
| − | * Authors | + | * Authors showed its applications on the problems where the relation of instances is not sequential. |
== Example Stacked Graphical Models Usage == | == Example Stacked Graphical Models Usage == | ||
'''text region detection''' | '''text region detection''' | ||
| − | * Task is to find the text regions in | + | * 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. | ||
Revision as of 22:32, 8 October 2010
Citation
Zhenzhen Kou and William W. Cohen. Stacked Graphical Models for Efficient Inference in Markov Random Fields in SDM-2007.
Online version
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.
