Recently, Spöck and Pilz (2010), demonstrated that the spatial sampling design problem for the Bayesian linear kriging predictor can be transformed to an equivalent experimental design problem for a linear regression model with stochastic regression coefficients and uncorrelated errors. The stochastic regression coefficients derive from the polar spectral approximation of the residual process. ...

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically, for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The problem is especially acute wh...

The ensemble Kalman filter (EnKF) is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts. While the accuracy of the forecast error covariance matrix is crucial for achieving accurate forecasts, the estimate given by the EnKF needs to be improved using inflation techn...

High-dimensional classification has challenges mainly due to the singularity issue of sample covariance matrix. In this work, we propose a different approach get more reliable matrix by adjusting estimated eigenvalues. This procedure also brings us nonsingular as by-product. We improve optimization obtain linear classifier incorporating adjusted and shrinkage mean vector into original problem. ...

− In this paper a development of an adaptive Kalman filter through a fuzzy inference system (FIS) is outlined. The adaptation is concerned with the imposition of conditions under which the filter measurement noise covariance matrix R or the process noise covariance matrix Q are estimated. The adaptive adjustment is carried out using a FIS based on the whiteness of the filter innovation sequence...