In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction model. We consider...

In this paper, a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data generating process. The model comprises a spatially filtered component including the origin,...

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects decision rules with a minimal set of features for classification of real-valued data. A rough set knowledge discovery framework is formulated for the...

This paper addresses the effect of liberalisation and transformation in Eastern Europe on international trade flows between specific West and East European countries belonging to the Rhine-Main-Danube area. The discussion is focused on volume and direction of trade effects using a simple, but...

This paper attempts to develop a mathematically rigid framework for minimizing the cross-entropy function in an error back propagating framework. In doing so, we derive the backpropagation formulae for evaluating the partial derivatives in a computationally efficient way. Various techniques of...

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. We provide an econometric derivation of the relationship and show that the presence of latent/unobservable regional knowledge capital leads to a model...

The analysis presented in this article focuses on seigniorage revenues in five Central and Eastern European Countries: Bulgaria, the Czech Republic, Hungary, Poland and Romania. A comprehensive discrete period accounting framework for measuring the sources and uses of seigniorage in the 1990s is...

An algorithm of incremental approximation of functions in a normed linearspace by feedforward neural networks is presented. The concept of variationof a function with respect to a set is used to estimate the approximationerror together with the weight decay method, for optimizing the size...

Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and...

Spatial interaction models of the gravity type are widely used to model origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterise an origin region of a flow, variables that...