Shivaswamy, Pannagadatta K and Bhattacharyya, Chiranjib and Smola, Alexander J (2006) Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. In: Journal of Machine Learning Research, 7 . pp. 1283-1314.
Restricted to Registered users only
Download (419Kb) | Request a copy
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 both classification and regression problems. Experiments show that the proposed formulations outperform imputation.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Journal of Machine Learning Research.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||30 May 2008|
|Last Modified:||19 Sep 2010 04:45|
Actions (login required)