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The Bayesian data reduction algorithm imposes a uniform prior probability mass function on discrete symbol probabilities.
A new data reduction algorithm for surface points is developed to preserve the original shape using an error metric based on a Hausdorff distance of principal curvature vectors.
The present study improves the data reduction algorithm of the needle probe method by shortening the measurement time on planetary surfaces by an order of magnitude.
For volume mesh deformation and two-way data interpolation over non-matching grids interface, a radial basis function methodology combined with a data reduction algorithm has been used.
In order to reduce the measurement time of blade in the machining process, a data reduction algorithm is proposed in this paper.
For these reasons, in the current paper we use PCA as the first step in our data reduction algorithm.
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All data reduction algorithms will be run in a high level software farm, and will have access to event information from all subdetectors.
Automated data reduction algorithms can use these measurements to improve the accuracy and continuity of established parameters, and to develop new diagnostic capabilities for a wider variety of ionospheric properties.
Feature selection and data reduction algorithms may further be used with the cleaned data.
Novel data reduction algorithms were used to identify gene signatures associated with tumor invasion and metastasis.
We further conclude that the development of novel data reduction algorithms for analysis of microarray studies is critical to allow for optimized mining of important, clinically-relevant datasets.
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