Shivaswamy, PannagadattaBhattacharyya, ChiranjibSmola, Alexander2009-05-222010-12-202009-05-222010-12-20Journal of Machine Learning Research 7.7 (2006): 1283-13141532-44351533-7928http://hdl.handle.net/10440/307http://digitalcollections.anu.edu.au/handle/10440/307We 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.32 pageshttp://www.sherpa.ac.uk/romeo/search.php "Author can archive pre-print (ie pre-refereeing) ... [but] cannot archive post-print (ie final draft post-refereeing) … [and] subject to Restrictions, 3 months for STM, author can archive publisher's version/PDF ... on institutional repository; Publisher copyright and source must be acknowledged; Must link to journal homepage; Publishers’ copyright statement must be included; Publisher's version/PDF must be used for post-print deposit." - from SHERPA/RoMEO site (as at 18/02/10)Keywords: Classification (of information); Data reduction; Method of moments; Problem solving; Regression analysis; Uncertain systems; Regression problems; Second order cone programming; Second order moments; Uncertain data; Computer systems programming2006-072015-12-08