The importance of good weighting methodology in information retrieval methods – the method that affects the most useful features of a document or query representative is examined. Good weighting methodologies are supposed to be more important than the feature selection process. Weighting features is the thing that many information retrieval systems are regarding as being of minor importance as ...

Weighting in life cycle assessment (LCA) is a controversial subject due to its dependence upon value judgements. One of the issues that has to be dealt with is whose values that shall be reflected. In a democratic society, a fair candidate to put forward would be the government. In environmental economics taxes and fees set up by governments to protect natural resources and the environment are ...

Areal interpolation is a geospatial analysis method that allows researchers to estimate the incidence of a phenomenon in one set of areal units given data based on different areal units. One practice implemented in conjunction with areal interpolation is known as filtered area weighting, in which ancillary data is introduced to exclude specific areas from the areal units based on certain criter...

INTRODUCTION: In dynamic susceptibility contrast (DSC) MRI experiments, the increase in the tissue transverse relaxation rate (∆R2) due to the passage of a bolus of intravascular paramagnetic contrast agent (CA) is routinely calculated using eqn. (3) below, neglecting signal enhancement effects due to T1 weighting. In this abstract, the condition for signal enhancement is analysed and an expres...

We present worst case bounds for the learning rate of a known prediction method that is based on hierarchical applications of binary context tree weighting (CTW) predictors. A heuristic application of this approach that relies on Huffman’s alphabet decomposition is known to achieve state-ofthe-art performance in prediction and lossless compression benchmarks. We show that our new bound for this...

Following a recent surge in using history-based methods for resolving perceptual aliasing in reinforcement learning, we introduce an algorithm based on the feature reinforcement learning framework called ΦMDP [14]. To create a practical algorithm we devise a stochastic search procedure for a class of context trees based on parallel tempering and a specialized proposal distribution. We provide t...

This paper presents two new ways of example weighting for subgroup discovery. The proposed example weighting schemes are applicable to any subgroup discovery algorithm that uses the weighted covering approach to discover interesting subgroups in data. To show the implications that the new example weighting schemes have on subgroup discovery, they were implemented in the APRIORI-SD algorithm. RO...

so far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. in order to evaluate annually precipitation spatial variation, this study was conducted in gilan province which precipitation is...

to identify important attributes/factors that contribute to grain and biological yields of wheat, 9912 sets of diverse data from field studies were extracted, and supervised attribute-weighting models were employed. results showed that when biological yield was the output, grain yield, nitrogen applied, rainfall, irrigation regime, and organic content were the most important factors/attributes,...