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And we tested the classification accuracy of convolutional autoencoders using different pooling approaches both with and without whitening transformation.
Firstly, we tested the classification effect for different P and Q with the proposed intelligent detection algorithm based on SVD and SVM.
We tested the classification accuracy of support vector machine (SVM) and position specific score matrix (PSSM) – the computational methods used by geno2pheno and WebPSSM respectively.
After reading and extracting data from each classification system, the two reviewers independently tested the classification using these 12 clinical cases.
An independent multidisciplinary group tested the classification.
We tested the classification performance of the features individually as well as in combination by using the three classification models.
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A simulation procedure was designed to test the classification power of each pipeline.
6) To test the classification performance of the trained algorithm on the test set.
J48 algorithm uses entropy function for testing the classification of terms from the test set.
A fivefold cross-validation scheme was performed to test the classification method.
Finally, a simple linear SVM classifier is used to test the classification performance.
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