Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in problems. Regarding the feature-weighted SVR algorithms, its contribution to model output taken into account. However, performance of subject feature weights and time consumption on training. In paper, an efficient proposed. Firstly, value constraint each weight obtained according maximal in...

The presence of heterogeneous variances is the norm in practice, which makes machine learning predictions less reliable when noise are implicitly assumed to be equal. To this end, we extend support vector regression by allowing a range variance functions model training. Specifically, as function mean and other variables traditionally used statistical modeling. This leads iterative between train...