Difference between revisions of "Z. Kou and W. Cohen. SDM 2007"
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The key points of the paper are: | The key points of the paper are: | ||
| + | * arbitrary base learner | ||
* 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 look at an image processing problem where the relation of instances is not sequential. | * Authors look at an image processing problem where the relation of instances is not sequential. | ||
| + | |||
| + | == Example Stacked Graphical Models Usage == | ||
| + | |||
| + | '''text region detection''' | ||
| + | * Task is to find the text regions in an image. | ||
| + | * | ||
Revision as of 22:13, 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.
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 look at an image processing problem where the relation of instances is not sequential.
Example Stacked Graphical Models Usage
text region detection
- Task is to find the text regions in an image.
