Efficient Structured Support Vector Regression
Support Vector Regression (SVR) has been a long standing problem in machine learning, and gains its popularity on various computer vision tasks. In this paper, we propose a structured support vector regression framework by extending the max-margin principle to incorporate spatial correlations among neighboring pixels. The objective function in our framework considers both label information and pairwise features, helping to achieve better cross-smoothing over neighboring nodes. With the bundle...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the International Image and Vision Computing New Zealand Conference (IVCNZ 2010)|
|01_Jia_Efficient_Structured_Support_2010.pdf||1.66 MB||Adobe PDF||Request a copy|
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