We propose a local boosting method in classification problems borrowing from an idea of the local likelihood method. The proposed method includes a simple device to localization for computational feasibility. We proved the Bayes risk consistency of the local boosting in the framework of PAC learning. Inspection of the proof provides a useful viewpoint for comparing the ordinary boosting and the...

In this paper we consider the topic of model reduction for nonlinear dynamical systems based on kernel expansions. Our approach allows for a full offline/online decomposition and efficient online computation of the reduced model. In particular we derive an a-posteriori state-space error estimator for the reduction error. A key ingredient is a local Lipschitz constant estimation that enables rig...

In this paper, we present some results on a posteriori error analysis of finite element methods for solving linear nonlocal diffusion and bond-based peridynamic models. In particular, we aim to propose a general abstract frame work for a posteriori error analysis of the peridynamic problems. A posteriori error estimators are consequently prompted, the reliability and efficiency of the estimator...

this paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. this estimation method is obtained by implementing ridge regression learning algorithm in the la- grangian dual space. the distance measure for fuzzy numbers that suggested by diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...

a method for error estimation of isogeometric analysis of plane stress problems which is the based on stress recovery and using the super convergent properties of the gauss points, has been already introduced. this paper is devoted to the development of the method and study of the effects of these supercovergent points on the solution improvement and error estimation of axisymmetric problems by...

in this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. the explicit solution is obtained for the hidden state estimation problem. finally, in a simulation study, the performance of the designed method ...

A novel and efficient technique is developed for estimating the local error per step when firstand second-order accurate projective integrators are applied to stiff multiscale systems. The estimation can be done on-the-fly; that is, the accumulated local error is readily estimated at the end and during the course of computing the solution at each outer time step. We demonstrate the effectivenes...