2005 SupportVectorRandomFieldsforSpa

• (Lee & al et al., 2005) ⇒ Chi-Hoon Lee, Russell Greiner, and Mark Schmidt. (2005). “Support Vector Random Fields for Spatial Classification.” In: Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases. doi:10.1007/11564126_16

Subject Headings: Support Vector Random Fields.

Cited By

• http://scholar.google.com/scholar?q=%22Support+vector+random+fields+for+spatial+classification%22+2005
• http://dl.acm.org/citation.cfm?id=2101235.2101253&preflayout=flat#citedby

Quotes

Abstract

In this paper we propose Support Vector Random Fields (SVRFs), an extension of Support Vector Machines (SVMs) that explicitly models spatial correlations in multi-dimensional data. SVRFs are derived as Conditional Random Fields that take advantage of the generalization properties of SVMs. We also propose improvements to computing posterior probability distributions from SVMs, and present a local-consistency potential measure that encourages spatial continuity. SVRFs can be efficiently trained, converge quickly during inference, and can be trivially augmented with kernel functions. SVRFs are more robust to class imbalance than Discriminative Random Fields (DRFs), and are more accurate near edges. Our results on synthetic data and a real-world tumor detection task show the superiority of SVRFs over both SVMs and DRFs.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 SupportVectorRandomFieldsforSpa	Chi-Hoon Lee Russell Greiner Mark Schmidt			Support Vector Random Fields for Spatial Classification				10.1007/11564126_16		2005