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This method can not only in processing uncertainty at coarser granularity, but also take into account both efficiency and generalization performance.
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This situation eventually affects the efficiency and generalization of SVM in classifying unseen testing samples.
A novel training method has been proposed for increasing efficiency and generalization of support vector machine (SVM).
Experimental results on large scale artificial datasets show that it compares favorably with a fully centralized implementation, in terms of speed, efficiency and generalization accuracy.
Both efficiency and outcomes may be compromised.
Both efficiency and accuracy are evaluated.
The employer mandate raises both efficiency and equity concerns.
However, competing aminoacyl-tRNAs, aminoacyl-tRNA synthetases, and release factors limit efficiencies and generalization.
Experimental results show that our method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.
The English word trip is an example of a term that has undergone both specialization and generalization.
Results suggest improvement in skill accuracy in both the training and generalization settings.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com