Second order cone programming approaches for handling missing and uncertain data
We propose a novel second order cone programming formulation for designing robust classifiers which can handle uncertainty in observations. Similar formulations are also derived for designing regression functions which are robust to uncertainties in the regression setting. The proposed formulations are independent of the underlying distribution, requiring only the existence of second order moments. These formulations are then specialized to the case of missing values in observations for...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|Shivaswamy_Second2006.pdf||419.23 kB||Adobe PDF|
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