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The baseline methods underperformed all other transfer learning approaches tested.
The baseline methods consist of decoding the data using the two acoustic models provided in the REVERB Challenge: the acoustic model trained with non-reverberant data (Clean-cond).
The results show that the proposed method achieves better performance than the baseline methods.
Experimental results show that the proposed methodology outperforms the baseline methods for disease prediction.
Experimental results on synthetic and real-world networks show that our algorithms outperform the baseline methods, in terms of both effectiveness and efficiency.
The obtained results indicate that our proposed method can identify the imaging biomarkers and diagnose the disease with favorable accuracies compared to the baseline methods.
Experiments we have conducted show that lexical clustering phase of I-TWEC can produce results with comparable clustering quality in a fraction of the time required by the baseline methods which use Longest Common Subsequence and Suffix Tree.
However, SDN-based LB outperforms the baseline methods.
First, the baseline methods were classified into four approaches.
Finally, we shortly explain the employed molecular encoding and the baseline methods used for comparison.
According to our experiments, the adopted approach presented significant gains over the baseline methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com