Partial, or set-valued classification assigns instances to sets of classes, making it possible reduce the probability misclassification while still providing useful information. This paper reviews approaches partial based on Dempster–Shafer theory belief functions. To define utility predictions, we propose extend matrix using an Ordered Weighted Average operator, allowing us model decision make...

This paper describes the effects of induced magnetic field and partial slip on the peristaltic flow of a couple stress fluids in an asymmetric channel. The two dimensional equation of couple stress fluid are simplified by making the assumptions of long wave length and low Reynolds number. The exact solutions of reduced momentum equation and magnetic force function have been computed in wave...

A partial least squares regression is proposed for estimating the function-on-function model where a functional response and multiple predictors consist of random curves with quadratic interaction effects. The direct estimation usually an ill-posed problem. To overcome this difficulty, in practice, data that belong to infinite-dimensional space are generally projected into finite-dimensional ba...

People employ the function-on-function regression to model relationship between two stochastic processes. Fitting this model, widely used strategies include functional partial least squares algorithms which typically require iterative eigen-decomposition. Here we introduce a route of based upon Krylov subspace. Our can be expressed in forms equivalent each other exact arithmetic: One is non-ite...

The function-on-function linear regression model in which the response and predictors consist of random curves has become a general framework to investigate relationship between functional predictors. Existing methods estimate parameters may be sensitive outlying observations, common empirical applications. In addition, these severely affected by such leading undesirable estimation prediction r...