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OpenMP balanced training SVM-SGD.

Hybrid MPI/OpenMP balanced training SVM-SGD.

Our balanced training SVM-SGD simultaneously uses the two approaches.

OpenMP version of balanced training SVM-SGD (Par-MC-SGD).

A balanced training algorithm for binary SVM-SGD classifiers,   2.

Hybrid MPI/OpenMP version of balanced training SVM-SGD (mpi-Par-MC-SGD).

Therefore, our balanced training SVM-SGD uses under-sampling the majority class (negative class).

The balanced training SVM-SGD also modifies the updating rule using the skewed misclassification costs.

These are parallel balanced training SVM-SGD using (T=20) epochs and regularization term (lambda = 0.0001).

AID2314 is a balanced training set with 37,055 active out of the 296,456 compounds present in it.

Since the original dataset was strongly imbalanced, a ensemble-based sampling procedure was applied to ensure balanced training sets.

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