The short-scan case in fan-beam computed tomography requires the introduction of a weighting function to handle redundant data. Parker introduced such a weighting function for a scan over pi plus the opening angle of the fan. In this article we derive a general class of weighting functions for arbitrary scan angles between pi plus fan angle and 2pi (over-scan). These weighting functions lead to...

Applicability of the weighting function method to gravity problems is discussed as compared with the exact method. The merits of the weighting function method are its simplicity and rapidity of computation, particularly in three-dimensional problems. For local geological problems, the weighting function method can be used for the gravity calculation with sufficient accuracy when the structure i...

data envelopment analysis (dea) is a nonparametric approach to estimate relative efficiency of decision making units (dmus). dea and is one of the best quantitative approach and balanced scorecard (bsc) is one of the best qualitative method to measure efficiency of an organization. since simultaneous evaluation of network performance of the quad areas of bsc model is considered as a necessity a...

A probability weighting function (w(p)) is considered to be a nonlinear function of probability (p) in behavioral decision theory. This study proposes a psychophysical model of probability weighting functions derived from a hyperbolic time discounting model and a geometric distribution. The aim of the study is to show probability weighting functions from the point of view of waiting time for a ...

We describe a sequential universal data compression procedure for binary tree sources that performs the " double mixture. " Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and unknown parameters. Computational and ...

Feature weighting algorithms try to solve a problem of great importance nowadays in machine learning: The search of a relevance measure for the features of a given domain. This relevance is primarily used for feature selection as feature weighting can be seen as a generalization of it, but it is also useful to better understand a problem’s domain or to guide an inductor in its learning process....