Title of Dissertation: ITERATIVE METHODS FOR THE STOCHASTIC DIFFUSION PROBLEM Darran G. Furnival, Doctor of Philosophy, 2008 Dissertation directed by: Professor Howard Elman Department of Computer Science It is the purpose of this thesis to develop iterative methods for solving the linear systems that arise from application of the stochastic finite element method to steady-state stochastic diff...

We compare four discretization methods, all based on entropy: the original C4.5 approach to discretization, two globalized methods, known as equal interval width and equal frequency per interval, and a relatively new method for discretization called multiple scanning using the C4.5 decision tree generation system. The main objective of our research is to compare the quality of these four method...

A key aspect of the recently proposed Hamiltonian Particle-Mesh (HPM) method is its time-staggered discretization combined with a regularization of the continuous governing equations. In this paper, the time discretization aspect of the HPM method is analysed for the linearized, rotating, shallow-water equations with orography and the combined effect of time-staggering and regularization is com...

We propose a new genetic fuzzy discretization method with feature selection for the pattern classification problems. Traditional discretization methods categorize a continuous attribute into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However...

We investigate the discretization of optimal boundary control problems for elliptic equations by the boundary concentrated finite element method. We prove that the discretization error ‖u∗−u∗h‖L2(Γ) decreases like N , where N is the total number of unknowns. This makes the proposed method favorable in comparison to the h-version of the finite element method, where the discretization error behav...

Rough set theory has become an important mathematical tool for dealing with uncertainty in data. The data discretization is one of the main problems to be solved in the process of synthesis of decision rules from table-organized data. In this paper, we present a new discretization method in the context of supervised training. This method is based on the neighborhood graph. To evaluate supervise...