Abstract We study a characterization of the precompactness sets in variable exponent Morrey spaces on bounded metric measure spaces. Totally are characterized from several points view for case over This is new constant exponents.

12:06 30th March 2004 Abstract The k -nearest neighbour (k -nn) model is a simple, popular classifier. Probabilistic k -nn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters. The k -nn classifier depends crucially on the metric used to determine distances b...

We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new metric based on the Minimal Description Length principle for choosing the threshold values for the discretization while learning the Bayesian network structure. This score balances the complexity of ...

In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data con­ taining both continuous and discrete vari­ ables. We describe a new technique for multivariate discretization, whereby each continuous variable is discretized while tak­ ing into account its interaction with the other variables. The technique is based on the use of a Bayesian...

An accelerating flat universe with a variable cosmological term is obtained in the Robertson-Walker metric. The variable cosmological term is defined by the correction terms of the metric tensor field. Simple solutions of the scale factor and the cosmological term are shown. In this model of the universe, the magnitude-redshift relation is analytically studied to see if the model reproduces the...