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As of May 2015, University IT announced a new set of classifications for Stanford data and systems: High Risk, Moderate Risk, and Low Risk.
The data gave rise to a set of classifications, which form part of the technique for the retrospective analysis of human error (TRACEr).
This new approach to training set design was evaluated against conventional approaches with a set of classifications of agricultural crops from satellite sensor data.
As of May 2015, a new set of classifications has been established and is now in effect for Stanford data and systems: Low Risk, Moderate Risk, and High Risk.
First and foremost, we need to develop a robust set of classifications to handle outcomes consistently.
Each sport has different physical demands and so has its own set of classifications.
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Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.
A set of classification criteria for transforming a discrete points mesh into the desired demolding map is introduced.
In this paper we establish the robustness of the CTC algorithm against a wide set of classification algorithms with explaining capacity.
Existing engineering geological and geotechnical data were combined with local observations to provide a unified set of classification criteria, consistent with provisions of the Greek Seismic Code EAK.
In essence, a set of classification rules can be regarded as a logical disjunction of rules, so that each rule can be regarded as a disjunct.
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