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Market research typically involves a great deal of mathematical modeling, and different types of research may rely on specifically devised methods of data collection, scaling, sampling, and data analysis.
This paper investigates the influence of different preprocessing techniques of attribute scaling, sampling, coding of categorical as well as coding of continuous attributes on the classifier performance of decision trees, neural networks and support vector machines.
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The results have implications for validation testing on scaled samples, predictive modelling and simulation, and armour design.
Finer geographically scaled samples often capture local atmospheric concentrations that are more relevant to human exposure.
One expected issue for measuring micro- or nano- scaled samples using synchrotron source is radiation damage.
Vertical lines highlight the approximate solid-solution compositions identified in the scaling sample.
Fig. 4 XRPD data (black line) and Rietveld models of the scaling sample.
According to this model, all solid compositions identified in the scaling sample would reflect non-equilibrium partitioning.
The scaling sample originates from the geothermal plant in Neustadt-Glewe in the state of Mecklenburg-Vorpommern in northern Germany (Degering et al. 2011).
Through multi-dimensional scaling, samples are represented as points before clustering through CLARA [ 51].
A total of 212 patients with full information were recruited for the validation and scaling sample.
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