The kernel null-space technique is known to be an effective one-class classification (OCC) technique. Nevertheless, the applicability of this method limited due its susceptibility possible training data corruption and inability rank observations according their conformity with model. This article addresses these shortcomings by regularizing solution Fisher methodology in context regression-base...

This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect heavy-tailed errors for high-dimensional robust regression with nonconvex regularization. When additive in linear models have only bounded second moments, we show that iteratively reweighted ?1-penalized adaptive Huber estimator satisfies exponential deviation bounds oracle properties, i...

A fuzzy regression model is used in evaluating the functional relationship between the dependent and independent variables in a fuzzy environment. Most fuzzy regression models are considered to be fuzzy outputs and parameters but non-fuzzy (crisp) inputs. In general, there are two approaches in the analysis of fuzzy regression models: linear-programmingbased methods and fuzzy least-squares meth...