Using a nonadditive set function to describe the interaction among attributes, a new nonlinear nonnegative multiregression is established based on Choquet integrals with respect to the set function. Regarding the values of the set function as unknown regression parameters, an evolutionary computation can be used to determine them when necessary data are available. Such a model is a generalizati...

The Open University's repository of research publications and other research outputs Forecast covariances in the linear multiregression dynamic model Journal Article

The Open University's repository of research publications and other research outputs Forecast covariances in the linear multiregression dynamic model Journal Article

The Open University's repository of research publications and other research outputs Forecast covariances in the linear multiregression dynamic model Journal Article

The Open University's repository of research publications and other research outputs Forecast covariances in the linear multiregression dynamic model Journal Article

The Open University's repository of research publications and other research outputs Forecast covariances in the linear multiregression dynamic model Journal Article

The Open University's repository of research publications and other research outputs Forecast covariances in the linear multiregression dynamic model Journal Article

This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative...

Cutting planes are a crucial component of state-of-the-art mixed-integer programming solvers, with the choice which subset cuts to add being vital for solver performance. We propose new distance-based measures qualify value cut by quantifying extent it separates relevant parts relaxed feasible set. For this purpose, we use analytic centers relaxation polytope or its optimal face, as well altern...

Abstract Most single‐image super‐resolution (SR) models suffer from the degradation of image restoration performance when restoring a high‐resolution (HR) low‐resolution (LR) downscaled using an unknown blur kernel. The spatially invariant kernel estimators have been proposed to predict address this issue. Nevertheless, variant exists in real‐world; thus, these are unsuitable for real‐world app...