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Following a manipulation of state, the subjects' judgment is probed by testing their classification of novel stimuli with sensory properties intermediate between the two trained stimuli.
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They did not test their classification method as mentioned by the authors as their future work.
Classification accuracies of subnetworks were found by testing their original sub-group (the group for which the subnetworks were found) and by cross-testing the remaining sub-group (the other half of the patients).
This assumption was experimentally validated by testing several classification methods (mixture of Gaussians, PCA, Parzen, and K-Means) in the initiation/termination modules yielding to a drop in the MOTA score proportional to their ability to correctly classify a blob as person/no-person.
The class I wrapper or group optimization algorithms evaluate a feature subset by testing its classification performance with a learning algorithm.
The diagnostic accuracy of a clinical test is typically evaluated by comparing the classification of individuals by the test with their classification by some diagnostic gold standard.
The predictive index was then evaluated by testing goodness-of-fit and classification accuracy.
The selection of predictors can be either preselected by testing or chosen based on classification algorithm, and the final identified predictors are also considered as differential expressed.
We further investigated whether new auditory classification learning can benefit the memory of a recently learned classification task by testing task-A performance on days 1 and 5.
For audio classification, it is computationally challenging to evaluate the features' properties by testing them individually [13] or analyzing their characteristics individually [14].
MiRNAClassify is compared with several state-of-the-art methods and some well-known classification models by testing the datasets about human, animal, and plant.
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